Early experiments building AEGIS a no fluff strategic AI console
Working on AEGIS a lightweight browser based AI console aimed at being a sharp, context aware thinking partner for founders
Right now it uses localStorage, Gemini Flash with basic anchor summarization when sessions get long. Still early stage, memory layer is basic.
Curious how others are handling long term context in browser based tools without heavy external services
Would value any thoughts.
Hi,
Awesome idea.
I tried the demo with a sample prompt: "social platform for endurance athletes".
Of course, it spun up an idea broken into several categories, like "Comparative Analytics" and "Monetization". I wonder if these are hardcoded...?
Anyway, it got me thinking (because I've been down this road, Clauding away my own project) - is it too optimistic?
It's a well-known issue with LLMs, which is why I go out of my way, when brainstorming ideas with Claude, to have it push back and be realistic. So, if you don't already have that angle, consider adding this "protect the founder from themselves" feature. It could be realistic by default (as opposed to overly optimistic), and/or you can add a configurable push-back scale. Although what founder would not like the maximum push-back? I would.
Cheers.
Interesting direction.
“No-fluff” is compelling, but I wonder if the challenge is less about removing features and more about making the output feel immediately usable.
A lot of AI tools give decent answers, but still require the user to translate them into actual decisions.
Curious what kind of output you’re optimizing for right now. More structured recommendations, or something closer to raw thinking support?
Thanks @Wonsik really appreciate the detailed feedback.
Right now I'm leaning more toward structured recommendations that help founders take immediate action, rather than pure raw thinking. The goal is to make the output feel usable quickly not just insightful.
This is a great problem to be thinking about early — long-term context is where most browser AI tools break 👍
Right now, localStorage + summarization is a good start, but it hits limits fast (size, structure, retrieval).
A few practical directions that work well without heavy infra:
Instead of storing full conversations, extract:
→ goals
→ decisions
→ key facts
Store them as small objects, not raw text.
Much easier to retrieve later.
Keep 3 layers:
→ Working memory (current session – localStorage)
→ Compressed memory (summaries of past sessions)
→ Key memory (important facts, user-defined or auto-detected)
Most tools fail because everything is treated equally.
Don’t store everything. Save only when:
→ user says “remember this”
→ a decision is made
→ repeated patterns appear
This keeps memory clean and useful.
Biggest unlock is not storing more — it’s retrieving the right thing.
Even simple keyword/tag matching can work early:
→ tag memories (“pricing”, “strategy”, “idea X”)
→ fetch relevant ones per query
Stay local-first, but keep a path for:
→ IndexedDB (better than localStorage for scale)
→ lightweight embeddings (only for key memory, not everything)
What you’re building (context-aware “thinking partner”) depends heavily on:
→ relevance of recall, not just persistence
If it remembers everything → noisy
If it remembers the right things → valuable
Curious:
→ are users mostly using it for ongoing thinking (same project)
or separate, one-off sessions?
That changes how memory should behave a lot.
Also, I’m running a small project (Tokyo Lore) where we test tools like this with real builders and see what actually sticks in daily use.
Since AEGIS is all about context + thinking, it could be a really interesting fit.
Happy to share more if you’re open 👍
Thanks @Tokyolore this is genuinely helpful feedback.
You're right retrieval is currently the weakest part. Even with layered summaries pulling the relevant context at the right moment without adding noise is tricky. Trigger based saving better structuring of key facts,decisions is exactly where I want to push next. Appreciate you breaking it down with the three layers and the emphasis on retrieval over storage. That makes a lot of sense. Also Tokyo Lore sounds like a solid project for testing tools with real builders. Happy to exchange thoughts.
“Glad it helped — and yeah, retrieval is where most tools quietly break, not storage.
If you get that layer right (clean signals + relevant recall), the whole ‘thinking partner’ idea becomes way more usable day-to-day.
On Tokyo Lore — we basically run small, focused rounds where builders use tools like yours in real workflows and we track what actually sticks vs what just sounds good in theory.
Since AEGIS is very usage-dependent (context + memory), it could be interesting to see how people actually use it over a few days.
Happy to share details if you want 👍”
Live demo: https://aegis-strategic-intelligence.vercel.app
Repo: https://github.com/ibrahimHossainn/aegis-console