AI sessions that learn
AI sessions that learn from each other
Temet helps one AI session improve the next by turning real corrections into reusable guidance. The result is simpler, steadier, and more useful over time.
The problem: every session starts from zero
Every AI coding session begins with a blank slate. Your agent does not remember what went wrong yesterday. It does not know which corrections you gave last week, which patterns kept failing, or which rules you had to repeat three times before they stuck. The result is predictable. You make the same corrections again. You explain the same constraints again. You watch the same mistakes happen again. Sessions do not compound. Each one is an island. This is not a minor inconvenience. It is a structural waste. The most valuable thing about working with an AI agent over time is the accumulated knowledge of how you work, what matters, and what to avoid. Without memory across sessions, that knowledge evaporates every time.
How Temet closes the loop
Temet breaks this cycle with a simple loop. After each session, Temet reviews what happened. It looks for the corrections you gave, the patterns that kept returning, the mistakes that repeated, and the decisions that clearly worked. From that, Temet creates a short session contract: a compact list of focus points, useful rules, and mistakes to avoid. It is not a long setup file and it is not more admin work for you. It is a small layer of practical guidance built from what just happened. The next session starts with that guidance already there. The agent begins with more context, and Temet can step in before a known mistake repeats. That is the real loop: one session leaves useful guidance behind, and the next session starts stronger because of it.
What a session contract looks like
A session contract is short, readable, and grounded in real work. It can include things like: Focus areas: check existing code before creating new files, keep changes smaller, verify work before closing the task. Rules: do not duplicate utilities that already exist, avoid silent fixes, prefer the simplest path first. Anti-patterns: creating new helpers too early, skipping verification, changing scope before validating the request. Success signals: fewer repeated corrections, fewer avoidable mistakes, more stable work across sessions. The important part is not the exact format. The important part is that it comes from your actual sessions. You do not need to maintain it by hand. Temet keeps updating it from what really happened.
When your experience becomes reusable
After enough sessions, this stops being just a private reminder system. It becomes a reusable layer of working knowledge. That is the exciting part. Another trusted agent can consult the same stable rules and patterns instead of starting from zero. An agent working with you can ask, "how does this person usually handle logging?" or "what kind of review discipline matters here?" and get an answer based on real sessions, not a stale document. Sharing is meant to stay selective and under your control. The point is not to expose everything. The point is to make the useful layer portable. That turns personal improvement into something bigger: reusable operational knowledge that can help future sessions and, later, other trusted agents too.
What this means for you
Less repetition. You explain fewer things again and again. More continuity. A good session leaves useful guidance for the next one. More trust. Your agent starts to reflect the standards you actually care about, instead of drifting every time a new session begins. Privacy first. Everything starts locally, and sharing stays optional. In plain English: Temet tries to help your AI sessions feel less random and more cumulative.
Why this stays accessible
The important thing is that you do not need to understand the whole system on day one. You can start with a simple local audit, see whether the result feels useful, and only then turn on tracking. That keeps the experience honest. Temet does not need you to adopt a giant workflow before you see value. It starts small, shows you what your sessions already prove, and then builds from there.
FAQ
How many sessions does it take before the contract is useful?
Typically two to three tracked sessions. Temet needs enough data to detect real patterns rather than one-time preferences.
Does the contract overwrite my existing CLAUDE.md?
No. Temet writes to a separate file (.claude/temet-rules.md). Your manual instructions remain untouched. You can review or edit any auto-generated rule.
Can I control what the Live Kernel shares?
Yes. Publishing is opt-in and granular. You choose which rules and patterns are visible. Nothing is shared without your explicit action.
Does this work with agents other than Claude Code?
Temet currently works with Claude Code and Codex session formats. Any agent that writes .jsonl session logs can be analyzed. Support for more tools is in progress.
Next step
Use this guide in practice with Temet's audit, tracking, and profile workflow.
Connect your agentPublished March 27, 2026