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CogniKernel
Give Claude Code and Codex memory that survives sessions
Your AI coding agent forgets every decision when the session ends. CogniKernel captures the decisions, constraints, and dead ends from each session and injects them into the next one — automatically. Runs fully local on small encoder models. No API keys, no tokens billed.
Hey Product Hunt 👋
I built CogniKernel because I kept having the same conversation with Claude Code.
Session ends, I come back the next day, and it wants to re-litigate a decision we
settled last week — or worse, it retries the exact approach we already found
doesn't work. I was hand-maintaining CLAUDE.md like a changelog and still losing
context.
Every memory tool I tried solved this by shipping my session transcripts to an LLM
to "summarize what mattered." That means another API key, per-session token cost,
and my code leaving my machine. That tradeoff didn't sit right.
So CogniKernel does it differently: extraction is *classification, not generation*.
Two small fine-tuned encoder models (~130MB ONNX) run locally on CPU in
milliseconds — one scores whether a sentence is worth remembering and what type it
is, the other detects when a new decision supersedes an old one. No API calls,
nothing leaves your machine. The only LLM involved is the coding agent you already
run.
A few things I'm proud of:
- Abandoned approaches go in a graveyard, so the agent doesn't re-try dead ends
- A "don't do X" rule surfaces at the moment you're about to do X — not three files later
- One store shared between Claude Code and Codex in the same directory
- Every hook is fail-open: memory can break without ever breaking your session
Benchmarked against flat notes and no-memory across four multi-session projects,
it made 2–4× fewer file reads in every single one.
It's Apache-2.0 and on PyPI: pip install "cognikernel[embedding]"
I'd love feedback from anyone running long-lived projects through Claude Code —
especially on what memory *should* be captured that I'm currently missing.
About CogniKernel on Product Hunt
“Give Claude Code and Codex memory that survives sessions”
CogniKernel was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #112 on the daily leaderboard. Your AI coding agent forgets every decision when the session ends. CogniKernel captures the decisions, constraints, and dead ends from each session and injects them into the next one — automatically. Runs fully local on small encoder models. No API keys, no tokens billed.
On the analytics side, CogniKernel competes within Productivity, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.7M followers on Product Hunt. The dashboard above tracks how CogniKernel performed against the three products that launched closest to it on the same day.
Who hunted CogniKernel?
CogniKernel was hunted by Kanishk Pratap Singh. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of CogniKernel including community comment highlights and product details, visit the product overview.
Kanishk Pratap Singh