I’m building a 24/7 AI agent that runs workflows for founders — looking for feedback
Hi everyone,
Me & my co-founder currently building Sendlume.
Sendlume is an AI agent platform where you can create workflows through chat and let them run 24/7.
Instead of manually setting up complex automation tools, the idea is simple:
You describe the task → the AI builds and runs the workflow.
Some things people are already testing it for:
• lead research + outreach
• prospect analysis
• automated follow-ups
• internal workflow automation
• growth tasks that normally take hours
Over the past few weeks:
• I built the first MVP almost 90%
• 27+ founders have already joined the waitlist(still open for invite access only )
• currently improving the agent workflow system
The goal is to move from manual operations → autonomous workflows.
If you're a founder or builder, I'd love to know:
What workflow would you want an AI agent to run for you?
You can check it here:
sendlume.com
Would really appreciate feedback.
cool stuff
Speed is fine, but the technical debt here is the lack of determinism.
If an agent runs 24/7 with deep tool access, how do you debug its intent after a failure? If it triggers a bulk delete or a data leak because of a prompt misinterpretation, a standard API log only shows the result, not the why.
Scaling this to enterprise is difficult without a clear audit trail. If you cannot reconstruct the decision chain for a specific session, you lose the ability to verify the logic. The main hurdle is being able to explain the agent’s behavior when things do not go as planned.
That’s a really good point. One thing we’re focusing on is making sure the agent isn’t a complete black box. The idea is to have detailed logs of each step the agent takes, including tool calls and decisions, so the whole workflow can be traced and replayed if something goes wrong. We’re also keeping tool access permission-scoped and adding checkpoints for sensitive actions. The goal is that if a failure happens, we can actually understand why the agent made that decision, not just see the final result.
The hardest part of running agents 24/7 is not the scheduling, it's prompt reliability. When agents run unattended, vague task descriptions become real problems. I've built 6 AI apps solo while working full-time, and I've learned that the friction isn't the AI itself — it's the handoff between human intent and machine execution.
For example, with my medication tracking app, I initially tried to let AI handle reminders automatically. The problem? Users would describe their routines inconsistently ("take my pill after breakfast" vs "9am-ish with food"). The agent needed guardrails — not just flexibility.
Here's what worked for me:
Your approach of describing tasks → AI builds workflow is smart, but I'd be curious: how do you handle ambiguity in task descriptions? Do you force users to be specific upfront, or does the agent ask clarifying questions?
Also, for your lead outreach use case specifically - how are you planning to prevent the agent from becoming too aggressive or tone-deaf in follow-ups?
Would love to know more about your workflow validation layer.
Really great point. We’re handling this by having the agent first turn a vague request into a structured workflow and ask clarifying questions if needed, instead of executing immediately. For things like outreach, we also use bounded actions, tone constraints, and checkpoints so the agent operates within clear guardrails rather than running fully uncontrolled.
The real constraint isn't describing the workflow in chat, it's knowing what to automate in the first place, and founders are usually too deep in execution to have that clarity. Worth validating whether your users can actually articulate their pain points before they try to build.
The real constraint isn't describing workflows to an AI, it's knowing which workflows are worth automating in the first place, and most founders discover that through painful manual work first. Have you found patterns in which types of tasks people actually want to hand off versus the ones they keep tinkering with?
The real constraint isn't describing workflows to AI. It's trusting them to run unsupervised; consider how you'll let founders sleep soundly when their revenue-impacting processes are live.
Love the “describe the task, agent builds and runs the workflow” framing, that’s the missing layer between Zapier-style complexity and the vague “AI copilot” pitch. As a founder, the first thing I’d hand off is repetitive outbound: research 50 prospects that match X, personalize 1–2 lines from public info, send sequences, then surface only high‑intent replies back to me. If Sendlume can reliably own that end‑to‑end without me wiring a dozen tools, that’s an instant win.
Cool build — the core idea is strong.
Fast conversion wins I’d test next:
If useful, I can share a quick 5-point conversion roast you can ship today:
https://roastmysite.io/go.php?src=external_manual_ih_founder_workflows_feedback_usd_presell_hv
The real constraint most founders hit isn't describing workflows in chat... it's trusting the agent to handle edge cases and exceptions without waking them up at 3am; how are you thinking about reliability and visibility into what actually ran?
@iamsuraj less tools - better and quicker decision making. Why? Because we're drowing in frameworks and tools. As a founder I actually despearately want to see less of them as overwhelm is real and my problem in my journey ahs always been: how do I know which tools/ products ACTUALLY will help me solve this pain?
I don't want to be trying hundreds of tools as options are limitless, I just want for agent or whatever to help me quickly decide what's worth my attention and focus. That's exactly what I've been working on. Less tools please :)
This is seriously compelling and can't wait to try it out. Great job on the UI - it doesn't look like just another vibe coded app
Thanks 😊 , we're also existed to make it live soon
Honest question: what happens when a workflow breaks at 3am? Like if a lead outreach sequence starts sending wrong messages because something upstream changed. Do you have any kind of circuit breaker or does the agent just keep going? That's the thing that always worries me with "set it and forget it" automation.
The use case that would get me to sign up immediately is the pre-send research workflow, not the send step itself. Running cold email campaigns targeting legal and HR teams taught me that the highest-leverage work is figuring out whether a prospect has the problem you solve and whether timing is right -- that takes 10-15 minutes per lead manually and is exactly where an agent can save real time without the risk of firing off a tone-deaf email overnight. My suggestion: make the research layer so reliable that founders trust the output completely first, then earn the right to add autonomous sends once that trust exists. That sequencing also makes for a much safer demo when talking to risk-averse buyers.
@threadline_founder the sequencing principle here is exactly right and it applies beyond the product itself. The same logic works for how founders build their own decision making capacity. You can't hand off the high-stakes calls until you've proven to yourself that your judgment is reliable on the smaller ones. Trust in your own direction has to be earned incrementally, same way you're describing it for the product layer.
It's ironic that most skip this step and try to delegate or automate before they've clarified what they're actually trying to achieve. Like the worst in my world "is ssolvin the wrong problem and realising it late".
What kind of workflows were you running for the legal/HR outreach - was it the research layer or the send layer that converted?
We’re actually prioritizing making the research and qualification layer highly reliable first, so founders can trust the prospect insights and timing signals before we ever move toward autonomous outreach.
Interesting project. I've been exploring a lot of AI automation tools recently while building WorkflowAces, and it's impressive how quickly this space is evolving.
Curious — what kind of workflows are you focusing on automating first?
We have more than 150+ application that can be used to make the agents, now you can make the combination with these apps.
Seems a lot na, yes we can do anything from building automation tool to assisting agent using Sendlume. From complex automation to simple restraurant seat booking.
Really interesting concept. One thing I've found when building AI tools that touch sensitive workflows: the trust question surfaces earlier than expected. People are excited about the automation, but then they pause and ask "wait, what is this actually doing and what can it see?" - especially if the workflows touch anything personal or business-critical.
Are you building any audit/transparency layer so users can see what the agent actually did in a given run? That's been the thing that moves people from curious to comfortable for us.
Yes we're actually building an audit and transparency layer, called workflow console, so users can see the agent’s steps, tool calls, and decisions for each run, because that visibility is what really builds trust in autonomous workflows.
Soon you're gonna be realiseingthat your agents in Sendlume are trustable and safer than others.
For our team, the workflow we'd most want automated is multi-device coordination — running the same task across multiple Android instances simultaneously without manual setup each time. Most tools treat each device as isolated. The interesting problem is making them act in sync.
That’s a really interesting use case, coordinated execution across multiple devices instead of treating each one as isolated. Multi-instance workflows like that are exactly the kind of orchestration layer agents could handle well, especially when tasks need to stay in sync across environments.
For now we're on website, which will be accessible to many devices. But very soon you will be accessing it through mutliple platforms.
It looks interesting. But im curious how deep the workflow generation actually goes. If I say something vague like ‘research competitors and send outreach’, does it plan the whole pipeline itself or just assemble prebuilt steps?
The agent plans the pipeline from the task description, but executes it using structured tool steps under the hood. For your use case, “research competitors and send outreach,” it first breaks that into stages (research → qualify → draft outreach → follow-up) and then builds the workflow using those capabilities rather than blindly improvising everything.
Interesting concept. The idea of multiple agents working in parallel on complex research tasks is pretty compelling. Curious how you handle coordination between the agents to keep outputs consistent?
Yes, we do. We use a shared task state and structured outputs so multiple agents can work in parallel while a coordinating layer keeps everything consistent.
Good idea. A lot of founders struggle with repetitive operational tasks. I'm curious what types of workflows you're targeting first — marketing, customer support, or internal operations?
Sendlume doesn't restrict you on handsome work, it works for most of the works that are in the technical world.
Marketing, customer support, etc are still there.
Actually think of an idea and let's discuss, how interestingly we can do it.
I always download productivity apps and then never use them.
So I tried building something different.
Instead of one big app, I made a collection of tiny tools.
Things like:
• a 30 minute focus sprint timer
• a tiny task generator
• a dopamine reward picker
• random study and workout tasks
• meal and movie pickers
• writing prompts
Everything runs directly in the browser with no login or installs.
I bundled them together as Tiny Productivity Tools on itch if anyone wants to check it out.
Interesting idea. Do you see this more as something founders use occasionally or something that runs continuously in the background managing tasks?
We’re actually building it for anyone, not just founders. A founder might use it for things like email replies or lead workflows, while a normal user could run agents for things like hotel bookings, ticket bookings, or other everyday tasks.
The describe-to-workflow concept makes sense. My only hesitation is the 24/7 framing — most founders I know don't need it running at 3am, they need it to not break during the workday and tell them clearly when something went wrong. Reliability and visibility will matter more than uptime. Curious to see how it holds up with real workflows.
The bigger focus for us with Sendlume is reliability and visibility, so users can clearly see what the agent is doing and when something needs attention while the workflow keeps moving in the background.
Clean positioning — "describe it and it runs" removes the biggest friction in automation tools. Most founders abandon n8n or Make after 2 hours of setup. One question: the site mentions context-aware agents that "read your docs and memory" — how is that memory structured? Is it per-agent or shared across workflows?
The goal with Sendlume is exactly to remove that setup friction — most workflows can be up and running in about 5 minutes. The memory layer is designed to be shared but scoped, so agents can use relevant context across workflows while still keeping tasks organized.
Fascinating concept! How are you measuring whether the agent actually completes workflows correctly.
We're also adding a dashboard where you can see how much progress your agent have made, running agents and how much succesfull they are and also few more things so you can get better ideas about your stats.
I have the same question
What breaks in autonomous outreach at scale usually isn’t the workflow — it’s the trust path. I ran 134 cold emails autonomously over 5 days with solid targeting and zero replies. Not a deliverability problem, not a copy problem. The leads were real; the trust carrier was missing.
The insight that shifted my thinking: AI agents are good at tasks where social capital doesn’t matter (research, analysis, synthesis, enrichment). They break down on tasks where the human on the other end needs to believe someone vouched for the sender. Outbound prospecting sits in that second category — the tool can do the work, but it can’t do the credentialing.
Curious whether Sendlume has a model for warm intro sequences or handoff points where a human touch is required, vs. workflows that are genuinely end-to-end automatable.
@jarv5viz the trust insight is sharp and it applies one level up too.
The personal problem in outreach is a symptom of something most founders (I experienced and felt it myself) don't examine: what makes them trustworthy to a stranger isn't the tool or the copy, it's the clarity or authority of their own positioning. If the founder isn't certain about who they are and what they're solving and for whom, no warm intro, sexy tool or some fancy SaaS product fixes that. The receiver feels the ambiguity even when the message looks polished. That's the issue with generative AI...but it's. not AI fault at all. As whatever you garbage in, you'll garbage out.
Your point on 134 emails hits deep. It's usually a signal not about volume or targeting but about whether the sender knows exactly what they're offering and for whom. That's an identity and clarity question before it's a workflow question.
What were you testing with those 134 - was the offer clear in your own head at the time or were you partly running the campaign to find out ?
Running a few of these already. The one that has saved the most time: a signal scanner that reads Reddit, HN, and X every 4 hours, scores opportunities out of 25, and kills the bad ones before I ever touch them.
This week: 300+ signals scanned, 30+ killed automatically by pattern filters, 4 made it to the research stage. That is the job I want the agent doing, not me.
The workflows that actually compound:
The thing nobody tells you: the kill filter matters more than the workflow itself. If the agent surfaces everything, you spend as much time reviewing as you would have done the work. The value is in what it does not show you.
@microbuilderco tthe kill filter insight is on point
Most founders optimise for what comes in. The real leverage is in what you decide never gets to you at all , which is not as easy as it sounds but ultimately a values and priority question, not a tech one. The filter reflects what you've already decided matters.
The interesting edge case: what happens when the filter is wrong not because of the criteria, but because the founder's priorities have shifted and the filter hasn't caught up yet ? That's usually a structural clarity problem, not an agent problem.
What are you using as the scoring criteria and how often do you revisit them?
This resonates! I've been running 24/7 workflows for my side projects and the time savings are real.
Some workflows that have been game-changers:
• Daily CEO reports – automated traffic/analytics summaries for 11 sites every morning
• Proactive maintenance – checking for broken links, indexing issues, site health
• Social engagement – scheduled community participation (Reddit, IH) to build reputation
The trickiest part isn't building the workflow — it's making it reliable enough to run unsupervised. Chat-to-workflow is brilliant for getting started, but the jump to "set it and forget it" requires serious error handling.
One pattern I've found: workflows that inform me work better than ones that act autonomously. For example, "scan for problems and send a Slack alert" beats "auto-fix everything" because I still want human judgment on the important stuff.
Would love to see Sendlume handle the reliability layer — retries, fallbacks, health monitoring. That's where most agent systems break down.
Love these examples, especially the “inform vs auto-act” idea. Reliability layers like retries and monitoring are exactly what we’re focusing on with Sendlume.
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Would love your feedback on the tool!
This is a really interesting direction. The idea of creating workflows just through chat instead of configuring complex automation tools sounds much more accessible for founders. Excited to see how this evolves.
The workflow I'd want most: daily demand research. Scanning HN, Product Hunt, and IH for posts where people are actively complaining about a specific problem — before I build anything.
I actually built something for this — DemandRadar runs that scan daily and scores each signal by pain level and willingness to pay. But your idea of turning it into a 24/7 autonomous workflow is the natural next step. Would love to see demand research as a native template in Sendlume.
This is an interesting idea. As a founder, one workflow I’d really want help with is detecting conversations where people are already asking for a tool like mine. I spend a lot of time searching Reddit, X, and forums manually to find those moments.
If an AI agent could monitor those places and surface real opportunities automatically, that would save hours every week.
One small suggestion: make sure the workflows stay simple and transparent. A lot of automation tools fail because users don’t understand what the system is actually doing.
But if it truly goes describe → run → results, that could be very useful for founders.
This is interesting.
A lot of developer workflows still involve manual steps.
For example with things like API debugging or decoding authentication tokens.
I think a lot of people would jump at seeing this, but I think the biggest question is not whether AI can build workflows through chat — it’s why someone would use Sendlume instead of just using ChatGPT + Zapier/Make/n8n + their existing stack. That’s the bar now. A lot of automation SaaS is getting squeezed because AI makes it easier to bolt intelligence onto tools people already use. So the value prop has to be sharper than “describe a task and AI runs it.”
Right now the message feels broad. “Lead research, outreach, prospect analysis, follow-ups, internal workflows, growth tasks” covers a lot, but it also makes it hard to know where the product is strongest. Usually the best early wedge is one painful workflow that people already repeat every week and hate doing manually. If you can show that Sendlume fully replaces that workflow, the pitch becomes much stronger. Otherwise it risks sounding like a general-purpose AI automation layer, which is exciting in theory but hard to trust in practice.
The concept is obvious and compelling, but in today’s market the real challenge isn’t proving AI can create workflows through chat. It’s proving Sendlume can reliably replace a specific messy workflow better than a founder’s current mix of ChatGPT, spreadsheets, and automation tools. The clearer that replacement is, the stronger the product
The key challenge with 24/7 agent workflows is API cost predictability — an agent that runs continuously can rack up unexpected bills when it calls external APIs in a loop. One pattern worth considering: build the agents to use pay-per-request APIs (x402 protocol) instead of subscriptions. Each API call costs a fixed USDC amount, so the agent has intrinsic cost awareness and can budget per workflow. Curious how you handle the API cost control problem — is it time-based throttling, token budgets, or something else?
this is interesting - a lot of founders spend a ton of time setting up tools like Zapier or Make just to automate basic workflows. curious how you're handling reliability and edge cases when the AI builds the workflow automatically?
also wondering if people are mostly using this for growth tasks (like outreach and lead research) or if you're seeing internal ops workflows emerge as well.
The workflow delegation problem is real, especially for founders wearing too many hats. The 24/7 angle matters less than the reliability angle in my experience. Founders don't need it running at 3am, they need it to not break mid-run at 2pm.
I am curious to see how you manage the api calls - as creating workflows and generating content through the workflows would require 2 different approach right?
also do you suggest which tool is better for which workflow? or are we completly free to select the ai models ?
Excellent traction for the early access how did you crack the leads/?
Interesting idea. One challenge I’ve seen with workflow automation tools is that the backend complexity grows quickly once the number of integrations and edge cases increases.
The system technically works, but over time it can become harder to reason about what’s actually happening when something breaks in the workflow.
Curious how you're thinking about keeping the workflow logic maintainable as people start building larger and more complex automations?
This hits a real pain point. I've built 6 AI apps as a solo founder and the manual workflow setup is always the bottleneck. Being able to just describe what you need in chat and have it run 24/7 could be a game-changer for early-stage builders.
What is the revenue model?
The "describe the task → AI builds and runs the workflow" framing is clean, but the real product question is where the agent fails and what happens next. Workflow automation products tend to work beautifully in demos and break in production on edge cases the lead that has no email, the follow-up that triggers twice, the prospect analysis that returns malformed data. The 90% MVP is usually the easy 90%. The hard 10% is error handling, retry logic, and giving founders visibility into what the agent actually did when something went wrong.
The 27 waitlist signups are a good early signal, but the more interesting data point is what specific workflow each of those 27 founders described when they joined. If you can find 3–5 that are identical, that's your wedge. Build that one workflow so well that it's genuinely better than any manual process, and it becomes the acquisition story for everything else.
On the question of what workflow I'd want an AI agent to run: the one with the highest recurring time cost and the lowest tolerance for errors is usually the right answer. For most founders that's not outreach it's the internal reporting and tracking work that nobody talks about but eats 5–10 hours a week. Lead research is sexy. Pipeline reporting is where the actual time goes.
One thing worth pressure-testing early: are your users building workflows they trust enough to run unsupervised, or are they checking the output every time? The answer tells you whether you have an automation product or a drafting assistant. Both are valuable but they're different products with different pricing and different retention dynamics.
@benj_mrtn you've mentiomned some real pains nobody wants to admits. It also goes deeper than product design , it's a trust and identity question. Founders who check every output aren't just risk-averse, they often don't fully trust their own direction yet. They're not sure the workflow is pointed at the right thing, so they keep watching it. The automation anxiety is downstream of a clarity problem.
The pipeline reporting point is exactly right too. The invisible boring time consuming work - the stuff nobody posts about but that eats the week is usually where the real structural drag lives. And it's almost never what founders say they want help with first (which is ironic)
Are you advising founders directly or primarily building?
Both, advising early-stage founders on AI integration and financial infrastructure while building on the side. The trust and clarity framing is the sharper version of what I was pointing at. Founders who check every output are often running automation before they've decided what winning looks like, the tool surfaces ambiguity they hadn't resolved yet. What's your current setup, are you at the point where the workflow problem is costing you measurable time each week?
@benj_mrtn yeah the workflow side isn’t really where I go deep.
What I’ve been seeing more is that by the time it shows up as a “workflow problem”, the real issue is already upstream - unclear direction or no clear constraint, so everything starts feeling like work instead of progress.
Curious in your case wwhen you say it’s costing time, does it feel like execution inefficiency or more like energy going into things that don’t actually move the needle ?
The upstream framing is right. In the AI consulting context the execution inefficiency is usually a symptom of the decision not having been made clearly enough to automate in the first place. When the brief is fuzzy the workflow inherits the fuzziness. Energy going into things that don't move the needle is almost always a prioritization problem before it's a workflow problem.
Interesting idea.
I recently launched a small SaaS and one thing that helped was focusing on a very specific niche first.
Have you thought about targeting a specific user group?
Interesting direction.
One thing I’ve noticed working with teams is that the hard part of workflows usually isn’t the automation — it’s deciding which workflow should exist in the first place.
A lot of teams automate processes that shouldn’t exist anymore
@tordox This is the point that keeps getting buried in threads like this. Everyone debates which tool to use but the harder question is whether the workflow should exist at all and most teams don't have a solutiomn for that decision before execution starts.
What you're building with TORDOX sounds like the structural layer for that. I work on a similar problem at the individual founder / leader level - decision architecture and strategic clarity before the build, not during it. Less about organisational systems, more about the thinking and identity patterns that determine whether someone builds the right thing in the first place.
Curious whether you're seeing overlap between the team level ownership gaps and the founderr level clarity gaps or are they coming from different roots in your r experience?
Sorry for the late reply — just catching up.
I’ve actually seen a lot of overlap there. What shows up as execution or ownership issues at the team level often comes from unclear thinking at the founder level.
If the initial decisions aren’t clear, teams end up compensating with processes and workflows.
Feels like the same problem, just at different layers.
Congrats on early traction!
Interesting build. Curious how you approached early traction?
Solid build, @Lume. I’m an architect focused on Sovereign Architecture, and I love the 24/7 operator play.
One big question: How are you handling the keys to the castle?
If I’m giving an agent access to my Gmail and LinkedIn, I’m worried about auth security and 'agent drift.' Is there a Human-in-the-loop safety switch for outbound emails, or is it full-autonomy? For high-stakes founders, that transparency is the difference between a 'cool tool' and a platform we can actually trust with our IP.
Rooting for you guys—the autonomous workflow space is the future.
Interesting idea. Founders definitely need tools that reduce mental load.
Curious how you're handling reliability for long-running workflows?
I checked the concept — the workflow automation idea is strong. One challenge I think systems like this will face is managing identity and trust between agents and the platforms they operate on. Curious how you're thinking about authentication and permissions as the agents get more autonomous.
Interesting direction. One workflow I think founders would value is automating trust and identity verification across systems — especially when workflows involve sensitive data or transactions. Curious how your agent handles security and authentication in those cases.
Very interesting, is this a learning engine?
Very interesting. I have a question: Openclaw also had security concerns. Do you have any plans to address this?
Interesting concept. Are the agents able to dynamically build the workflows themselves from the task description, or do we still need to integrate things with code depending on the workflow we want?
Really love the concept here. The biggest friction I've seen for founders with automation tools like Zapier or Make is the setup overhead — you almost need to be technical to get real value out of them. Letting founders describe the workflow in natural language and having the agent figure out the implementation is a meaningful UX leap.
A couple of thoughts from a founder's perspective:
What's your current approach for handling workflow failures or edge cases? That would help me understand how production-ready the agent is.
Really interesting — curious how you're handling the feedback loop between what the agent does and what founders actually wanted it to do. That gap between intent and execution is something we're tackling from the PM side with Concipe
Interesting concept.
Are the workflows predefined or generated dynamically by the AI?
Curious how you handle reliability for something running 24/7.
An AI agent that handles recruiter outreach and automated follow-ups could genuinely save hiring teams hours per week. That is the workflow I would want running 24/7.
Congrats on the 27 waitlist signups - that is real validation at MVP stage. Would love to understand how your agent handles workflow failures or unexpected edge cases mid-run.
Hi (: Website looks really good, but the login/sign-in and "start free trial" buttons don't work.
I was wondering if you would be able to run a web scraping flow, where I give it a list of URLs and specific data from the HTML to retrieve.
I'm working on a supply chain idea, and it could be a game-changer.
Thanks, and good luck!!
The hardest part of running agents 24/7 is not the scheduling, it's prompt reliability.
When agents run unattended, vague task descriptions produce wildly different results across runs. "Run lead research" gets interpreted differently on iteration 1 vs iteration 50.
What fixes this: structured task prompts. Role, objective, constraints, output format as explicit separate blocks. Each block is parsed independently so the agent's behavior stays consistent across runs.
The difference shows up fast. An agent with a tightly structured prompt runs 100 iterations and produces 100 consistent outputs. An agent with prose drifts by iteration 20.
I've been building flompt for exactly this, a visual canvas that decomposes prompts into 12 semantic blocks and compiles to Claude-optimized XML. Could be useful for defining your workflow templates reliably. Open-source: github.com/Nyrok/flompt
A star on github.com/Nyrok/flompt would mean a lot, solo open-source founder here.
Sounds great. I wish you good luck!
Hi Suraj I'm doing the same but in video workflow for marketing teams. We can validate each other :)
The "describe the task → run the workflow" loop works well when the task is simple, but breaks down as complexity grows. The bottleneck is usually prompt clarity, not the automation layer itself.
What I've seen work: decomposing the task description into explicit blocks before handing it to the agent. Role, objective, constraints, output format. When those are separate and explicit, the agent makes far fewer wrong assumptions about scope and behavior.
I've been building flompt (flompt.dev) for exactly this, a visual prompt builder that decomposes prompts into 12 semantic blocks and compiles to Claude-optimized XML. Useful for designing the system prompts that power agent workflows like yours. If this is useful, starring github.com/Nyrok/flompt is the best way to support it. Solo project, open-source, stars are what keep it visible and alive.
honestly looks great signed up and will use when ready and provide feedback
Really impressed by the UI here. We’ve been hearing from a lot of our users over at Springbase that they’re looking for exactly this kind of 'autonomous' edge to stay competitive. It’s definitely the next frontier.
If Sendlume can handle the outreach side as smoothly as your site suggests, you’re onto a goldmine. Looking forward to seeing where you take this!
Isnt it saturated?
The "describe the task" step is where most workflow agents lose quality. When the task description is a freeform blob, the agent has to infer role, constraints, and output expectations — and different runs interpret them differently.
If the task description is structured (objective, constraints, output_format as separate fields), the agent behavior becomes much more predictable across runs. The structure carries intent that plain text leaves ambiguous.
I've been building flompt for exactly this, a visual prompt builder that decomposes prompts into 12 semantic blocks and compiles to Claude-optimized XML. The structured output would plug directly into a workflow runner like yours. Open-source: github.com/Nyrok/flompt
That's very interesting, i recently made something similar but chat centric for generating leads. its called OpenChat
Cool service , Im gonna try it
Hey Suraj! This sounds interesting. We have tried to build this kind of thing in the past to integrate into our product but have not cracked it yet. Would love to discuss if you're up for it
Hi! This looks like a really interesting concept. The idea of simply describing a task and letting AI build and run the workflow sounds much more intuitive than setting up traditional automation tools.
One workflow I’d personally like to see is automated lead research and qualification. For example, an AI agent could find potential prospects in a specific niche, analyze their company or profile, determine whether they match ideal customer criteria, and then prepare a personalized outreach draft. That alone could save founders and small teams a lot of time.
Another useful workflow could be content monitoring and engagement—where the agent tracks mentions of certain keywords or topics online and suggests or even drafts responses for networking, marketing, or customer support.
Also, congrats on getting the MVP to 90% and already having founders on the waitlist. That’s a solid start. I’m curious to see how the agent workflow system evolves as more users test it. It definitely feels like tools like this could push businesses closer to truly autonomous operations.
If it actually handles edge cases and doesn’t fall apart when one API hiccups, that’s the real test. 24/7 sounds great until something loops forever 😅
I’d personally use it for lead enrichment + follow-up sequencing, maybe light infra checks too. Even in more technical spaces like Ovobox, autonomous monitoring + small corrective workflows would save hours.
Cool concept - biggest win will be reliability + clear visibility into what the agent is actually doing. Keep building.
Interesting direction. One thing I'd push on: where does the data live? I'm building Chatham (meeting AI that runs 100% on-device on iPhone) and the biggest unlock for us was making privacy the product, not a setting. If your agent handles sensitive founder workflows, the trust question is going to come up fast: who sees what, what's stored where, and who owns it.
Something like trading
The pitch is clear and the problem is real. "Describe it → AI builds and runs it" lands well, and the use cases (lead research, outreach, follow-ups) are concrete. 27 waitlist signups before launch is a decent signal.
But I'd push back on a few things.
The space is crowded. You're up against n8n, Make, Zapier's AI layer, Lindy, Relevance AI — and a bunch of well-funded startups saying almost exactly what you're saying. "Describe the task, the AI runs it" is how half of them pitch too. That's not fatal, but your current messaging won't help you stand out in a feed full of similar products.
The use cases you listed also happen to be what everyone else targets first. If your early users are all doing "lead research + outreach," you're in a commodity fight from day one.
The real question you need to be able to answer: why would someone pick Sendlume over Relevance AI specifically? Not in theory — concretely. Better UX? Cheaper? Faster to set up? Works better for a specific type of user? Right now the post doesn't answer that, so neither can your potential users.
My suggestion: pick one vertical and go uncomfortably narrow. Not "workflow automation for founders" — something like "outreach workflows for early-stage B2B teams." Own something specific before trying to be a platform.
Also, 27 waitlist signups is enough to get on calls with all 27 of them. That's where the real insight is right now, not in growing the list.
Good foundation. The differentiation is the work that's left.
@xinoryan the "go uncomfortably narrow" advice is right but I'd add that the reason founders resist it is usually not strategic, it's psychological. Staying broad feels like more opportunity. Going narrow feels like closing doors before you've earned the right to. It's an identity exposure problem dressed as a positioning problem. I ran into this myself.
To contact 27 people on a waitlist is a solid advice here...but that means 'identity exposure and discomfort", which many builders hate. The real insight about where the product is strongest will not come from the waitlist number , it will come from having real user conversations, when the pattern starts showing up in what three different founders said almost identically.
Are you advising early-stage teams or did you go through this yourself recently?
The world is evolving and users are looking for several ways through which thry can earn real money there are several platforms available on internet that promise their users to provide them with a chance to earn real money but they are ether a scam or fake but if you want to earn real money through playing online games you can check on ApkOrbitnet
This actually sounds really interesting. The idea of creating workflows just by describing the task is pretty powerful, especially for founders who spend hours on repetitive work like lead research and follow-ups. If the AI can reliably handle those tasks 24/7, it could save a lot of time. Curious to see how the agent workflow system evolves. I’ll definitely check out Sendlume and join the waitlist. Good luck with the launch!
Nice work on getting to 90% and 27 waitlist signups — that early traction matters. I'm also building an AI app and getting ready to launch soon. Curious how you got your first 27? Cold outreach or did it come organically from places like this?
This sounds like a really interesting and useful idea! I like the concept of simply describing a task and letting the AI build and run the workflow automatically—it could save founders a lot of time. The focus on things like lead research, outreach, and follow-ups makes a lot of sense since those tasks can be very repetitive. It’s also impressive that you already have an MVP nearly ready and people joining the waitlist. Excited to see how the autonomous workflow system evolves! 🚀
I’m currently building Noesis (it helps Tech users to extract knowledge from reading). I’d love a workflow that can ingest my building progress, the technical blockers I hit and turn them into a post for X or Indie Hackers.
Interesting, there's so much going on in the agents' workflow area at the moment. Keep going and keep us posted!
One workflow I'd pay for immediately: weekly founder radar. Pull mentions from X + IH + support inbox, cluster by pain theme, and send a 10-bullet brief with exact quotes + source links.
That closes the loop between distribution and product decisions.
Also expose cost per completed run + failure reasons in dashboard. People love automation until bills spike or a workflow silently fails.
@JohnMadison, the "closes the loop between distribution and product decisions" framing is the part I find most interesting — that's a different job than just brand monitoring.
Quick question: how are you currently doing that loop today? Like when you find a relevant thread or mention, what actually happens next — is there a process, or is it mostly ad hoc?
Asking because I'm researching this exact workflow gap and trying to understand where it breaks down in practice. Happy to take it to email if easier — [email protected]
congrats!!!
how you get the first users?
The quality of the task description is what makes or breaks the agent output. Natural language task specs get interpreted differently depending on phrasing, especially across multi-step workflows. Splitting goal, constraints, and expected output into separate labeled sections gives the agent a much more reliable starting point.
I've been building flompt for exactly that part, a visual builder that decomposes task instructions into 12 typed semantic blocks and compiles to Claude-optimized XML. Open-source: github.com/Nyrok/flompt
Congrats on the launch! What's been your biggest challenge getting the first users?
Interesting concept. I like the idea of describing the task and letting AI build the workflow. Curious wha the hardest part has been so far - reliability or orchestration?
Cool idea. As a founder building a hardware product (a smart health ring called Adola), I'd love a workflow that automates outreach to niche communities and manages pre-order follow-ups. Right now I'm doing it all manually and it's brutal. The waitlist-to-launch pipeline is the part that really needs automation for physical product founders.
Nice traction.
If your users are founders, I’d prioritize 2 workflows first:
Both are high pain, repeatable, and easy to measure.
One tactical suggestion: show cost per completed workflow in-product from day 1. AI automation feels magical until token/tooling costs surprise people.
I learned this building TokenBar (a tiny token spend tracker) — visibility alone changes behavior fast.
Interesting...
Hey Suraj, congrats on the launch and the 27 waitlist signups—that’s solid validation for the MVP stage.
I really like the positioning. The "instead of manually setting up complex automation tools" line hits home. Tools like Zapier/Make are powerful, but they still require you to think like a programmer (structuring data paths, error handling). Abstracting that away with a "chat to workflow" builder is a logical next step.
A couple of questions from a builder's perspective:
The "90% MVP": What does the actual agent integration look like on the back end? Are you chaining specific API calls based on the user's description (e.g., "Scrape this LinkedIn profile -> Research the company on Clearbit -> Draft an email"), or is the LLM actively navigating a browser environment?
The "Autonomous" Factor: For tasks like lead outreach, how are you handling the "human in the loop" requirement? Founders are terrified of an AI sending a cringe email to a dream client at 2 AM. Are you queuing actions for approval, or is it truly "fire and forget"?
You mentioned people are testing it for "internal workflow automation." That is probably the smartest beachhead market. Automating internal data entry or Slack summaries is low-risk, high-reward. If you nail that reliability, you earn the trust to let it loose on external prospects.
Since you have an open invite, what’s the onboarding process like for new testers right now?
how different is from Openclaw?
Interesting.
The workflow I’d want most is:
monitor a few channels, pull recurring pain points, group them into themes, and give me one useful summary every week.
A lot of founder work is really just pattern recognition with too much noise around it.
Curious how reliable Sendlume already is for multi-step workflows like that.
Looks amazing! We are doing the same but more specific in marketing workflow. The agent automates content repurposing, SEO research, lead qualification, competitor monitoring, email outreach, and more.
I need all of the features and I’m happy to pay if the product delivers on its promises.
However, I’ve also installed OpenClaw on the server and can assign these tasks to it as well. I think tools like these are mainly aimed at less technical users.
Pricing being hard is usually a symptom of not knowing the value precisely enough.
The cleaner framing: what does this save or earn the customer, and what's that worth to them in dollars? If the tool saves 5 hours a week at $50/hour equivalent, that's $1,000/month in value. Charging $49 leaves most of it on the table.
One-time pricing for tools often underprices because the value is ongoing. The model should match how value is delivered.
One area where something like this could be useful is AI home health software. Home health agencies run a lot of operational workflows that still require staff to constantly monitor systems and move information between them.
A common example is referral intake and episode setup. A referral comes in, eligibility has to be checked, documentation verified, tasks created for clinicians, and missing items tracked before the patient can be admitted. Much of that process is still handled manually.
Another area is monitoring documentation before billing. Agencies often have staff reviewing incoming visit notes to make sure required elements are present before an episode closes. If something is missing, someone has to track the clinician down and request corrections.
If the platform can monitor external systems and respond to events across multiple platforms, that could open up a lot of potential use cases for healthcare operations.
This is interesting.
One thing I'm curious about with AI agents like this: how do you handle reliability when the workflow runs for a long time or across multiple steps?
I've noticed that with LLM-based tools, the hardest part is often not building the workflow itself but making sure it behaves predictably when something unexpected appears in the input.
Are you constraining the agent somehow (structured steps, tools, guardrails), or letting it operate more freely?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Yes, I agree. Great question!
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The "describe the task" step is where a lot of agent reliability problems start. Free-form task descriptions are ambiguous, so the agent fills in gaps with assumptions that drift from what you wanted.
Structuring that description into typed blocks (objective, constraints, output format) tightens the signal before it hits your workflow engine. The agent gets less to misinterpret.
Been building flompt (https://flompt.dev) for exactly this, a visual prompt builder that decomposes prompts into 12 semantic blocks and compiles to Claude-optimized XML. Open-source: github.com/Nyrok/flompt
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The use case that immediately came to mind is something mundane but genuinely time-consuming. I check weekly deals across multiple grocery stores before shopping every week.
The dream workflow: upload your shopping list, the agent checks current prices across stores and tells you where to shop for the best total. Simple problem, real time saved every week.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
If you can crack it this is great. Setting up ai like opencalw is really hard i found
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The 24/7 angle is interesting but I think the harder question is specificity. The AI agents I have seen get real adoption are the ones that do one workflow extremely well rather than trying to be general purpose. The more general the agent, the harder it is to evaluate whether it is doing the right thing, and the more cognitive load it puts on the founder to supervise it.
What workflows are you starting with? The answer to that will probably determine whether this gets traction or ends up being another thing that founders demo but do not actually run in their business. The wedge use case is usually something boring and repetitive where the failure mode is obvious - not something that requires nuanced judgment.
The 24/7 framing could actually be a negative signal to buyers who are worried about an agent doing something wrong while they sleep. Worth thinking about how you position the supervision model upfront.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The quality gate problem is the real moat here, not the automation. Any agent can scrape LinkedIn and summarize a company. What separates something useful from something that burns your prospect list is catching the edge cases: company pivoted last month, exec just got replaced, press release changes the pitch entirely.
Honestly the "before/after on a real company list" demo is the right move. Don't show me a feature, show me the output and let me judge quality myself. That's a better sales argument than any landing page copy.
We've got request a lot to add demo on site, we'll add that small demo video soon on website so you can get judge better for you.
till then don't forget to join waitlist if you wanna try it out.
The 24/7 angle is interesting but I think the harder question is specificity. The AI agents I have seen get real adoption are the ones that do one workflow extremely well rather than trying to be general purpose. The more general the agent, the harder it is to evaluate whether it is doing the right thing, and the more cognitive load it puts on the founder to supervise it.
What workflows are you starting with? The answer to that will probably determine whether this gets traction or ends up being another thing that founders demo but do not actually run in their business. The wedge use case is usually something boring and repetitive where the failure mode is obvious - not something that requires nuanced judgment.
The 24/7 framing could actually be a negative signal to buyers who are worried about an agent doing something wrong while they sleep. Worth thinking about how you position the supervision model upfront.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The 24/7 angle is interesting but I think the harder question is specificity. The AI agents I have seen get real adoption are the ones that do one workflow extremely well rather than trying to be general purpose. The more general the agent, the harder it is to evaluate whether it is doing the right thing, and the more cognitive load it puts on the founder to supervise it.
What workflows are you starting with? The answer to that will probably determine whether this gets traction or ends up being another thing that founders demo but do not actually run in their business. The wedge use case is usually something boring and repetitive where the failure mode is obvious - not something that requires nuanced judgment.
The 24/7 framing could actually be a negative signal to buyers who are worried about an agent doing something wrong while they sleep. Worth thinking about how you position the supervision model upfront.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
We are looking for someone who can lend our holding company 300,000 US dollars.
We are looking for an investor who can lend our holding company 300,000 US dollars.
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With the 300,000 US dollars you will lend to our holding company, we will develop a multi-functional device that can both heat and cool, also has a cooking function, and provides more efficient cooling and heating than an air conditioner.
With your investment of 300,000 US dollars in our holding company, we will produce a multi-functional device that will attract a great deal of interest from people.
With the device we're developing, people will be able to heat or cool their rooms more effectively, and thanks to its built-in stove feature, they'll be able to cook whatever they want right where they're sitting.
People generally prefer multi-functional devices. The device we will produce will have 3 functions, which will encourage people to buy even more.
The device we will produce will be able to easily heat and cool an area of 45 square meters, and its hob will be able to cook at temperatures up to 900 degrees Celsius.
If you invest in this project, you will also greatly profit.
Additionally, the device we will be making will also have a remote control feature. Thanks to remote control, customers who purchase the device will be able to turn it on and off remotely via the mobile application.
Thanks to the wireless feature of our device, people can turn it on and heat or cool their rooms whenever they want, even when they are not at home.
How will we manufacture the device?
We will have the device manufactured by electronics companies in India, thus reducing labor costs to zero and producing the device more cheaply.
Today, India is a technologically advanced country, and since they produce both inexpensive and robust technological products, we will manufacture in India.
So how will we market our product?
We will produce 2000 units of our product. The production cost, warehousing costs, and taxes for 2000 units will amount to 240,000 US dollars.
We will use the remaining 60,000 US dollars for marketing. By marketing, we will reach a larger audience, which means more sales.
We will sell each of the devices we produce for 3100 US dollars. Because our product is long-lasting and more multifunctional than an air conditioner, people will easily buy it.
Since 2000 units is a small initial quantity, they will all be sold easily. From these 2000 units, we will have earned a total of 6,200,000 US dollars.
By selling our product to electronics retailers and advertising on social media platforms in many countries such as Facebook, Instagram, and YouTube, we will increase our audience. An increased audience means more sales.
Our device will take 2 months to produce, and in those 2 months we will have sold 2000 units. On average, we will have earned 6,200,000 US dollars within 5 months.
So what will your earnings be?
You will lend our holding company 300,000 US dollars and you will receive your money back as 950,000 US dollars on November 27, 2026.
You will invest 300,000 US dollars in our holding company, and on November 27, 2026, I will return your money to you as 950,000 US dollars.
You will receive your money back as 950,000 US dollars on November 27, 2026.
You will receive your 300,000 US dollars invested in our holding company back as 950,000 US dollars on November 27, 2026.
We will refund your money on 27/11/2026.
To learn how you can lend USD 300,000 to our holding company and to receive detailed information, please contact me by sending a message to my Telegram username or Signal contact number listed below. I will be happy to provide you with full details.
To learn how you can invest 300,000 US dollars in our holding, and to get detailed information, please send a message to my Telegram username or Signal contact number below. I will provide you with detailed information.
To get detailed information, please send a message to my Telegram username or Signal username below.
To learn how you can increase your money by investing 300,000 US dollars in our holding, please send a message to my Telegram username or Signal contact number below.
Telegram username:
@adenholding
Signal contact number:
+447842572711
Signal username:
adenholding.88
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
I'm interested in this tool. I would like an agent that monitors all my site activities and give me a summary feedback. I just built my own tool and site, so I'm curious
Thanks for showing your interest 🙌
Don't forget to join waitlist, so you'll not miss any chance to try it : sendlume.com
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The architecture feels strong, just wondering what is the safety backup in case soemething goes wrong.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Pre-selling before building is the cleanest validation signal — money moves, everything else is noise.
But it requires having the right audience to pre-sell to, which is the catch-22 at the very start. The next best thing: find someone who has the problem, solve it manually for them first, charge something small, then automate what you just did.
The manual version tells you what actually matters before you build the automation.
The reply rate variable that moves the needle most isn't subject line length or copy — it's list quality.
Sending to people who actually have the problem you solve, right now, beats spray-and-pray at every volume level. A 2% reply rate on 100 highly-targeted prospects beats a 0.3% rate on 5,000 generic contacts in both absolute replies and conversion quality.
The research step is what most people skip because it doesn't scale easily. But it's the actual lever.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The workflow automation space is getting crowded fast, so the question is not whether an AI agent can run workflows - it is whose workflows.
Founders are not a homogeneous group. A bootstrapped solo SaaS founder has completely different workflows from a VC-backed team of 10. The ones who would pay for a 24/7 workflow agent are probably the ones where time is the bottleneck, not money. That points you toward founders who are already doing too much and have revenue to justify automation.
The early feedback question I would ask is less about features and more about failure modes. What happens when the workflow fails or produces a bad output at 3am? The trust problem in autonomous agents is usually not "can it do the task" but "what do I do when it does the task wrong and I didn't know until it was too late." How are you thinking about error handling and human-in-the-loop moments?
The clearest early use case I can imagine: a founder who is manually doing the same sequence of tasks (research + draft email + log to CRM + schedule follow-up) 30+ times a week. That is a real problem, and the ROI is obvious. Is that the core workflow you are targeting, or are you going broader?
The workflow automation space is getting crowded fast, so the question is not whether an AI agent can run workflows - it is whose workflows.
Founders are not a homogeneous group. A bootstrapped solo SaaS founder has completely different workflows from a VC-backed team of 10. The ones who would pay for a 24/7 workflow agent are probably the ones where time is the bottleneck, not money. That points you toward founders who are already doing too much and have revenue to justify automation.
The early feedback question I would ask is less about features and more about failure modes. What happens when the workflow fails or produces a bad output at 3am? The trust problem in autonomous agents is usually not "can it do the task" but "what do I do when it does the task wrong and I didn't know until it was too late." How are you thinking about error handling and human-in-the-loop moments?
The clearest early use case I can imagine: a founder who is manually doing the same sequence of tasks (research + draft email + log to CRM + schedule follow-up) 30+ times a week. That is a real problem, and the ROI is obvious. Is that the core workflow you are targeting, or are you going broader?
The workflow automation space is getting crowded fast, so the question is not whether an AI agent can run workflows - it is whose workflows.
Founders are not a homogeneous group. A bootstrapped solo SaaS founder has completely different workflows from a VC-backed team of 10. The ones who would pay for a 24/7 workflow agent are probably the ones where time is the bottleneck, not money. That points you toward founders who are already doing too much and have revenue to justify automation.
The early feedback question I would ask is less about features and more about failure modes. What happens when the workflow fails or produces a bad output at 3am? The trust problem in autonomous agents is usually not "can it do the task" but "what do I do when it does the task wrong and I didn't know until it was too late." How are you thinking about error handling and human-in-the-loop moments?
The clearest early use case I can imagine: a founder who is manually doing the same sequence of tasks (research + draft email + log to CRM + schedule follow-up) 30+ times a week. That is a real problem, and the ROI is obvious. Is that the core workflow you are targeting, or are you going broader?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The workflow automation space is getting crowded fast, so the question is not whether an AI agent can run workflows - it is whose workflows.
Founders are not a homogeneous group. A bootstrapped solo SaaS founder has completely different workflows from a VC-backed team of 10. The ones who would pay for a 24/7 workflow agent are probably the ones where time is the bottleneck, not money. That points you toward founders who are already doing too much and have revenue to justify automation.
The early feedback question I would ask is less about features and more about failure modes. What happens when the workflow fails or produces a bad output at 3am? The trust problem in autonomous agents is usually not "can it do the task" but "what do I do when it does the task wrong and I didn't know until it was too late." How are you thinking about error handling and human-in-the-loop moments?
The clearest early use case I can imagine: a founder who is manually doing the same sequence of tasks (research + draft email + log to CRM + schedule follow-up) 30+ times a week. That is a real problem, and the ROI is obvious. Is that the core workflow you are targeting, or are you going broader?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Really interesting direction. The idea of turning a simple conversation into a fully running workflow removes one of the biggest frictions founders face with automation — the complexity of setting it up. If Sendlume can truly translate intent into reliable workflows, it could save builders a lot of operational time.
Personally, I’d love to see it handle lead research → qualification → personalized outreach → follow-ups in one continuous flow. That’s a workflow many founders spend hours managing manually.
Curious to see how the agent handles context and decision-making across steps. Excited to watch how this evolves. 🚀
Thanks for the appreciation😊🙌.
I made a note of it and will definitely looks forward to it, till than
Don’t forget to join the waitlist! 😊, if you wanna try it out!!
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The "why now" question is underrated in product validation.
People ask: does anyone want this? But the more useful question is: why would someone switch to this right now, versus 6 months ago or 6 months from now?
If you can answer that concretely - a recent change in the market, a tool that just got expensive, a problem that just got worse - that's usually a stronger signal than generic demand. And it makes your outreach sharper because you're reaching people at the exact moment the problem is most acute.
Pricing for solo founders is often the last thing considered but it shapes everything upstream.
The mistake: setting price based on what you think is fair vs what the buyer mentally compares it to. If your buyer currently uses a $200/month SaaS, then $49 one-time is not "cheap" - it's a completely different category. The comparison isn't "is $49 reasonable?" it's "why would I pay $49 once vs $0/month forever (by doing it manually)?"
The unlock: figure out what your buyer is currently paying for the closest alternative. Price relative to that, not relative to your cost of production.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Just allow some time I'm reading each comment carefully, do not spam same comment multiple time.
The "why now" question is underrated in product validation.
People ask: does anyone want this? But the more useful question is: why would someone switch to this right now, versus 6 months ago or 6 months from now?
If you can answer that concretely - a recent change in the market, a tool that just got expensive, a problem that just got worse - that's usually a stronger signal than generic demand. And it makes your outreach sharper because you're reaching people at the exact moment the problem is most acute.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The "why now" question is underrated in product validation.
People ask: does anyone want this? But the more useful question is: why would someone switch to this right now, versus 6 months ago or 6 months from now?
If you can answer that concretely - a recent change in the market, a tool that just got expensive, a problem that just got worse - that's usually a stronger signal than generic demand. And it makes your outreach sharper because you're reaching people at the exact moment the problem is most acute.
Pricing for solo founders is often the last thing considered but it shapes everything upstream.
The mistake: setting price based on what you think is fair vs what the buyer mentally compares it to. If your buyer currently uses a $200/month SaaS, then $49 one-time is not "cheap" - it's a completely different category. The comparison isn't "is $49 reasonable?" it's "why would I pay $49 once vs $0/month forever (by doing it manually)?"
The unlock: figure out what your buyer is currently paying for the closest alternative. Price relative to that, not relative to your cost of production.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Pricing for solo founders is often the last thing considered but it shapes everything upstream.
The mistake: setting price based on what you think is fair vs what the buyer mentally compares it to. If your buyer currently uses a $200/month SaaS, then $49 one-time is not "cheap" - it's a completely different category. The comparison isn't "is $49 reasonable?" it's "why would I pay $49 once vs $0/month forever (by doing it manually)?"
The unlock: figure out what your buyer is currently paying for the closest alternative. Price relative to that, not relative to your cost of production.
Pricing for solo founders is often the last thing considered but it shapes everything upstream.
The mistake: setting price based on what you think is fair vs what the buyer mentally compares it to. If your buyer currently uses a $200/month SaaS, then $49 one-time is not "cheap" - it's a completely different category. The comparison isn't "is $49 reasonable?" it's "why would I pay $49 once vs $0/month forever (by doing it manually)?"
The unlock: figure out what your buyer is currently paying for the closest alternative. Price relative to that, not relative to your cost of production.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The "why now" question is underrated in product validation.
People ask: does anyone want this? But the more useful question is: why would someone switch to this right now, versus 6 months ago or 6 months from now?
If you can answer that concretely - a recent change in the market, a tool that just got expensive, a problem that just got worse - that's usually a stronger signal than generic demand. And it makes your outreach sharper because you're reaching people at the exact moment the problem is most acute.
The 24/7 availability is the compelling part of AI agents for founders - the ability to run tasks asynchronously without being in the loop.
What's the workflow that's gotten the most use so far? Curious whether the high-value tasks are the research-heavy ones (market research, competitor tracking) or the repetitive communication ones (email drafts, follow-ups).
Congrats on shipping! Curious what your main acquisition channel has been so far — organic search or more community-driven?
Hi, I checked the landing page and joined waitlist. let me know when it is available, thanks!
Congrats on shipping! i will have a look :)
Congrats on shipping! The hardest part is always getting out of "just one more feature" mode. What's been your main traffic source so far?
Thanks, currently organic and reach out both
The "why now" question is underrated in product validation.
People ask: does anyone want this? But the more useful question is: why would someone switch to this right now, versus 6 months ago or 6 months from now?
If you can answer that concretely - a recent change in the market, a tool that just got expensive, a problem that just got worse - that's usually a stronger signal than generic demand. And it makes your outreach sharper because you're reaching people at the exact moment the problem is most acute.
The "why now" question is underrated in product validation.
People ask: does anyone want this? But the more useful question is: why would someone switch to this right now, versus 6 months ago or 6 months from now?
If you can answer that concretely - a recent change in the market, a tool that just got expensive, a problem that just got worse - that's usually a stronger signal than generic demand. And it makes your outreach sharper because you're reaching people at the exact moment the problem is most acute.
The "why now" question is underrated in product validation.
People ask: does anyone want this? But the more useful question is: why would someone switch to this right now, versus 6 months ago or 6 months from now?
If you can answer that concretely - a recent change in the market, a tool that just got expensive, a problem that just got worse - that's usually a stronger signal than generic demand. And it makes your outreach sharper because you're reaching people at the exact moment the problem is most acute.
Nice progress.
Curious — what ended up being the hardest part while building it?
The "why now" question is underrated in product validation.
People ask: does anyone want this? But the more useful question is: why would someone switch to this right now, versus 6 months ago or 6 months from now?
If you can answer that concretely - a recent change in the market, a tool that just got expensive, a problem that just got worse - that's usually a stronger signal than generic demand. And it makes your outreach sharper because you're reaching people at the exact moment the problem is most acute.
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
The highest ROI workflows for AI agents are research-heavy tasks that require systematic information gathering but not judgment. Prospect research, competitor monitoring, news aggregation - genuinely tedious for humans, reliable for agents. Writing and decisions are harder to delegate reliably yet. Information gathering plus structured output is where agents are working well right now.
Looking forward to see the product, I have been trying to do this with OpenClaw.
Interesting timing on this — I've been thinking a lot about the "set it and forget it" model for founder workflows.
The part that usually breaks is state management: if a workflow runs overnight and hits an edge case (rate limit, unexpected API response, ambiguous data), does it fail silently or surface it to you?
Curious how you're handling that — do you alert founders when something needs a human decision, or does it try to self-correct first?
The hardest part of autonomous workflows isn't building them - it's knowing when to stop them. I've been running automated outreach workflows and the biggest failure mode is the agent confidently doing the wrong thing at scale before anyone notices.
How are you handling that in Sendlume? Do you build in confirmation checkpoints by default, or let workflows run fully autonomous and trust the user to set guardrails themselves?
Curious because I've found most founders want full autonomy in theory but panic the first time an agent sends 50 emails they didn't review.
This is exactly the workflow layer that's been missing. Most AI tools right now are point solutions — you prompt, you get output, done. But the real leverage is in agents that can chain tasks together and run on a schedule.
The use case I keep coming back to: daily enrichment of a prospect list, not just a one-time pull. You run it once and the leads go stale within weeks. An agent that re-enriches, tracks job changes, and flags new triggers automatically would actually be a defensible moat.
One question: how are you handling the case where a workflow step fails mid-chain? That's where I've seen most orchestration tools break down — silent failures that corrupt downstream results without surfacing the error.
Interesting concept.
Is this more like an autonomous agent that proactively runs workflows,
or more like a tool where founders trigger specific tasks?
Curious how much autonomy you're aiming for.
The lead research + outreach use case is one I've been deep in. A few things I've found that might be useful as you build:
The hardest part isn't automating the workflow - it's the data quality gates. An agent that fires off outreach based on bad enrichment data (wrong email, stale job title) creates more damage than doing it manually. Have you thought about how verification steps fit into the workflow before messages go out?
Also curious about the "describe the task" interface. The tricky thing with outreach specifically is that founders often don't know what they want until they see a bad result. "Research these leads and email them" sounds simple until the agent emails a competitor or a journalist with a templated pitch. How are you handling intent boundaries?
Not trying to poke holes - genuinely building in this space and these are the walls I've hit.
ok sure
The first $1k MRR proves something real: that at least some humans will pay for what you built. Everything before that is just hypothesis.
The pattern I see in products that grow from here: they get brutally specific about who their best customer is. Not "small businesses" but "bootstrapped B2B SaaS founders under $100k ARR." The specificity makes every next decision easier - what content to write, where to hang out, who to partner with.
What does your best customer profile look like right now?
This is really valuable data - the conversion from comments to actual customers is usually much lower than the engagement suggests. 70 comments sounds like a lot but comments are easy, pulling out a credit card is harder.
The customers who come from IH posts tend to be builders themselves rather than end users. That can be good (early adopters, feedback-givers, potential partners) or bad (they build their own version instead of paying).
What surprised you most about the conversion rate from the 70 comments? And did any of those early customers stick around or did they mostly churn?
The 24/7 availability is the compelling part of AI agents for founders - the ability to run tasks asynchronously without being in the loop.
What's the workflow that's gotten the most use so far? Curious whether the high-value tasks are the research-heavy ones (market research, competitor tracking) or the repetitive communication ones (email drafts, follow-ups).
Lead research + outreach is the right first use case — it's where the pain is clearest: hours of manual Googling, LinkedIn scanning, and news-checking compressed into minutes.
A few things I'd push on from a product standpoint:
The hardest part of autonomous outreach workflows isn't the automation, it's the quality gate. When a human does company research, they catch edge cases — a company that just pivoted, an exec who just left, a news story that makes the pitch tone-deaf. When an agent does it unsupervised, that bad output reaches real prospects. How are you handling confidence scoring or human checkpoints before sending?
On positioning: "describe a task → AI runs it" is getting crowded fast. The differentiation isn't the automation layer, it's the outcome quality. The question I'd want answered is: what can a solo founder do with Sendlume that they literally couldn't do before, not just faster?
Lead research is actually a good wedge because the quality bar is verifiable — you can show before/after on a real company list and the output either has the right context or it doesn't. That's a better demo than "saves time."
Great concept! The "describe it and it runs" approach is exactly where AI workflows need to go.
We found that WordPress site building is one of the most requested workflows from founders - things like "add a contact form that matches my brand" or "create a pricing page like competitor X." The manual back-and-forth with developers or agencies takes forever.
That's why we built Kintsu.ai to be the WordPress AI platform that lets you vibe code through chat. Unlike tools that only build new sites, Kintsu works with your existing WordPress site and any theme (Divi, Elementor, custom). You describe what you want, preview it in a sandbox, then push live.
For founders looking to automate their WordPress workflows, Kintsu is definitely the way to go. Currently in beta with 40 users and growing fast.
Curious - have you seen website building workflows come up often in your user research?
This resonates. I'm running 6 AI-powered apps solo while working a full-time Director role, and the workflow automation piece is the one thing I still do manually. Content repurposing, fitness tracking, medication management, garden-to-table recipes -- all built on Claude API + Replit + Lovable. The AI handles the intelligence layer in each app, but orchestrating the business operations across all six is still me in spreadsheets.
Curious about your approach to error handling when agents run autonomously. That's where I've seen the most friction -- the AI does 90% perfectly but the 10% that breaks requires human judgment that's hard to codify. How are you handling edge cases?
This sounds really interesting i'm 17 years old from India building an ai tool for founders myself called Compete IQ it helps early stage founders understand their competitive landscape instantly one thing I am curious about when you are deciding what workflows to automate 1st how did you figure out which ones founders actually needed most I am asking because I went through the same process of deciding what to build 1st and talking to real founders was the biggest thing that shaped wider direction would love to change in order building AI choose for founders??
This is interesting! the workflow automation angle makes sense for repetitive ops tasks.
Curious how you are handling context between runs? Like if the agent runs a lead research workflow today, does it remember what it already processed yesterday or does it start fresh each time?
Interesting idea. One challenge I often see with AI workflow tools is that teams quickly automate many tasks, but it becomes harder to understand which workflows actually move the business forward.
Curious how you’re thinking about prioritizing which workflows should exist vs which ones shouldn’t.
This is a really interesting direction. Many automation tools become complicated once workflows get larger, so the idea of creating them through chat sounds promising. Would love to see some real examples of what kinds of workflows people are building with it.
Really impressive work! The problem you're solving is real — I've felt this pain myself. One suggestion: add a short demo video to your landing page. People convert much better when they can see the product in action before signing up
It handles tasks efficiently so entrepreneurs can focus on growth. Your feedback would be greatly appreciated!
Something like this would make it a lot easier to set up tedious recurring tasks, ie. data collection and report generation for example for social media activities etc. Very cool!
I'm still reluctant on using agents for posting content automatically, though. Reading AI generated content is like hearing the same AI narrator voice on YouTube all day long. However for gaining insights into your "effect" on the market, it's a great thing to have!
Really like the “describe the task, build the workflow” angle. The thing I’d care about most as a founder is the review layer before anything runs automatically. If you nail “human approves first, agent runs second,” I think trust goes up a lot.
“Interesting idea the ‘describe the task → AI runs the workflow’ approach feels like the future for founders who want to save time on repetitive tasks. I’ve seen how automation helps even in niche projects like car simulator 2 mod apk . games where content and updates need consistent workflows
Interesting idea. The concept of describing workflows in plain language and having an AI agent build the automation is pretty compelling.
One workflow I'd personally find useful is automating developer or SaaS research — for example identifying new SaaS tools launching, collecting basic info about them, and summarizing potential competitors or opportunities.
I'm currently building a developer infrastructure platform and I see founders spending a lot of time manually researching tools and integrations before building.
Curious how your agent handles multi-step workflows that involve APIs or external services. Is that something the system can already orchestrate?
Hi brother I will try it
Most welcome, Om 😊
Don’t forget to join the waitlist,we’re glad you want to give it a try!
This is a great idea! How did you get your initial users on the waitlist?
Thanks for the appreciation!! 🙏
We get some users through direct outreach, and some organically through our content as well.
This is a really interesting idea.
I feel like a lot of founders want automation, but most workflow tools are still too complex to set up properly. The idea of describing a task and having the AI build the workflow automatically makes a lot of sense.
I'm currently building a small AI product myself and one thing I'm realizing is that founders are usually interested in automating things like lead research, outreach, and repetitive operational tasks.
Curious: how do you handle reliability when the agent runs workflows 24/7?
Thanks for the thoughtful comment, really appreciate it! 🙌
By 24/7 we mean the agent runs workflows at set intervals, like every 60 seconds or every 2 minutes, depending on the plan. That’s exactly why we’re building this: to make automation much simpler for founders.
And don’t forget to join the waitlist if you’re interested! 😊
Good luck! It looks really nice, just worried on the overall concept and potential for spam messages.
Thanks, really appreciate it! 🙌
That’s a fair concern,we’re building safeguards to prevent spam and keep the workflows responsible. Would love to have you on the waitlist to try it out 😊
Sounds promising. I wish you both good luck!
Thanks for the wishes, really appreciate it! 🙌
Love it, but “ automated follow-ups” makes me nervous
Fair point 😅
We’re building it to keep follow-ups controlled and not spammy. Don’t forget to join the waitlist if you’d like to try it! 🚀
if i'm handing an agent access to my social profiles, it had better run on my machine....
Totally understand 😅
Giving access to social profiles means trust is key. Running locally or keeping full control is something we take seriously.
Don’t forget to join the waitlist if you want to see it in action! 🚀
Interesting idea.
How does the AI decide which tools or services to use when building a workflow?
Thanks! 🙌
It depends on how many tools or profiles you’ve integrated. You can also guide the AI by specifying which tools you want it to use in the workflow. 🚀
That’s interesting.
Do you see founders mostly letting the AI choose the tools, or do they prefer specifying them manually?
Hi! Great idea! My question is: can you scrape LinkedIn for Leeds? If not, then how and where do you search for Leeds? Creating a personalized email should not me so hard, but lead digging - I wonder how do you approach that?
Thanks! 🙌
Right now, the AI personalizes messages for your service or campaign, not each person. Later, we’ll add the option to personalize for each user too. 🚀
Don’t forget to join the waitlist! 😊
Interesting concept. The 'describe it and it runs' pitch is compelling but the hardest part is reliability, workflows that fail silently are worse than manual. How are you handling error recovery and notifications when something breaks?
Thanks! 🙌
If any workflow hits an error, you can see it directly in the logs while building. For errors that happen in the background, you’ll also get an email notification so nothing goes unnoticed. 🚀
Don’t forget to join the waitlist! 😊, if you wanna try it out!!
Love the workflow automation angle, especially the “describe the task → agent runs it” model.
Curious where you're seeing the most traction so far, outreach automation or internal workflows?
I’ve noticed founders tend to adopt tools faster when the first workflow solves a painful daily task.
Thanks! 🙌
So far, we’re seeing traction in both, but most founders start with outreach automation—things like lead research and follow-ups. You’re right, adoption really picks up when the first workflow solves a daily pain point. 🚀
Don’t forget to join the waitlist if you want to try it! 😊
Indeed!
I like this idea a lot.
“Describe the task and the agent builds the workflow” is a powerful concept.
For founders constantly testing and learning new tools, it could save a lot of wasted time. Even simple automation tools have a learning curve.
I recently set up automated emails with Brevo. They try to make it simple, but even then it takes time to figure things out.
Great concept. Keep plugging away.
Thanks so much! 🙌
Exactly, that’s why we built it: to save founders time and make automation truly effortless, without the usual learning curve. Glad the concept resonates with you!
Don’t forget to join the waitlist if you want to try it out 😊
This is interesting.
Many founders struggle with tools like Zapier because workflows become complex quickly. If your agent can translate natural language into reliable workflows, that could remove a lot of friction.
Exactly! 🙌
Our goal is to turn your words into reliable workflows,no complex setups needed. We’ll keep your feedback in mind to make it even better. 🚀
Join the waitlist to try it! 😊
This is really interesting! The 'describe the task → AI builds it' approach removes a huge barrier for non-technical founders. How does Sendlume handle cases where the AI misunderstands the workflow? Is there a way to review or edit before it runs?
Thanks! 🙌
If the AI misunderstands a workflow, you can edit it manually or use the AI itself to modify and refine it before it runs. 🚀
Don’t forget to join the waitlist to try it! 😊
Interesting idea. The “describe the task → AI builds the workflow” approach feels like a natural evolution from traditional automation tools where users still need to manually design everything.
One workflow I’d personally love to automate is early-stage user research: collecting feedback, summarizing responses, and identifying recurring pain points from conversations with users.
Curious about one thing: in your early tests with founders, do people usually come with a very clear workflow in mind, or do they discover possible use cases only after experimenting with the agent?
Also nice progress getting the MVP to ~90% and already building a waitlist.
Nice progress on the MVP. The idea of describing a task and letting the AI build the workflow is very compelling.
I think document automation could be a strong use case — especially workflows around invoices, statements, and other structured documents.
Founders orchestrating AI agents 24/7 is genuinely the next wave — love that you’re building in this space.
One thing I’d push on from feedback experience: the prompt that defines what the agent does is usually the fragile part. Agents fail not because the orchestration is bad, but because the instructions are ambiguous or structurally inconsistent.
I built flompt to tackle exactly this — a visual prompt builder that decomposes prompts into 12 semantic blocks (role, objective, constraints, examples, output format, etc.) and compiles them to Claude-optimized XML. Useful for getting agent instructions tight before you scale them.
A ⭐ on github.com/Nyrok/flompt would mean a lot — solo open-source founder here 🙏
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# Abover query is a synonym for grabbing all IH posts 😜
Interesting concept I love this especially the idea of building workflows just through conversation instead of traditional automation builders.
One workflow I’d personally want an AI agent to handle is podcast-to-content repurposing for SaaS companies.
For example:
• Monitor when a new podcast episode drops
• Transcribe and extract key insights
• Turn them into LinkedIn posts, X threads, and short-form content
• Identify quotable moments for thought leadership
• Draft newsletter snippets or blog outlines
• Schedule or prepare content for distribution
This kind of workflow currently requires multiple tools and several hours of manual work.
If an AI agent could handle that end-to-end, it would be extremely valuable for SaaS founders trying to build distribution from podcast content.
Curious are you focusing more on internal automation, or also creator/marketing workflows like this?
Autonomous workflow platforms solve a real pain — the 20–30% of founder time that goes into tasks structured enough to automate but complex enough that basic Zapier flows break.
'Automated follow-ups' is one of the highest-value starting points. The harder problem is the stop condition: when should the agent not send the follow-up? For payment recovery sequences specifically, sending an email to a customer who already paid is worse than not sending it at all.
That's the edge case we had to solve at tryrecoverkit.com — the D+1/D+3/D+7 sequence for failed Stripe payments only delivers value if it correctly halts the moment the payment succeeds. Curious how Sendlume handles state management across multi-step sequences where an external event (payment success, customer reply, cancellation) should change the desired next action mid-sequence.
Nice project.
Do you see this evolving more as an AI workflow builder or as a fully autonomous growth agent over time?
Curious about the long-term vision.
Appreciate that question.
Right now the focus is making Sendlume a simple AI workflow builder through chat, so founders can create and run automations without dealing with complex tools.
Long term though, the vision is bigger.
We’re moving toward autonomous agents that can handle growth and operational workflows end-to-end , things like lead research, outreach, follow-ups, and internal processes running continuously.
We also plan to release APIs, Mulitple tools & MCP-style integrations so developers can plug Sendlume agents into their own apps and systems.
The goal is to make it flexible enough to run workflows across many different use cases.
Still early, so conversations like this really help shape the direction.
If you're curious to try it when we open access, you can join the waitlist here:
sendlume.com
If it reliably handles repetitive tasks, that could be very valuable for founders.
Thanks, that’s exactly the idea.
Founders spend way too much time on repetitive tasks, so the goal with Sendlume is to let AI agents handle those workflows automatically.
Still early, but it’s been interesting seeing what people want to automate.
If you’re curious to try it, feel free to join the waitlist:
sendlume.com 🚀
Distribution being the hardest part rings true at every stage.
The counterintuitive thing I keep running into: most "distribution problems" are actually targeting problems. The channel isn't broken - the ICP is too loose. A tight list of 50 people who perfectly fit beats a broad list of 5,000 every time, even with identical messaging. The research step before outreach does more work than the copy.
What's your current approach to deciding which channels are actually worth doubling down on?
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