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Mengram 2.47
Memory that survives /clear, compaction and the next agent
Seven months after the first launch, this one is about the work, not memory types. Hooks for Claude Code, Codex and Cursor: recall on every prompt, save after each turn, a checkpoint before compaction that puts the working state back. New: `mengram resume`, a task card (done, remaining, last test and its commit) the next session or agent picks up, keyed by repo and branch. Every fact says which tool wrote it and when. Benchmark: 23-62x fewer context tokens than sending history, losses published.
Hi PH, Ali here, solo maker. I launched Mengram seven months ago as "an AI memory API with three types". That was the wrong pitch: nobody buys memory types. What people actually lose is the work. Claude Code compacts and forgets which files it just edited, a new session re-explains the project, Cursor and Codex don't know what Claude decided yesterday, and the next agent redoes a step that was already done.
So this version is about that, and it comes with numbers instead of adjectives.
What's new since March:
- Hooks for Claude Code, Codex and Cursor: recall on every prompt, save after each turn, a checkpoint before compaction that puts the working state back (files you edited, last commands, your last request).
- `mengram resume`: a task card written when an agent stops (done, remaining, last test and the commit it ran on), keyed by repo + branch so another session, another agent or another machine picks it up.
- Every fact says where it came from (tool, session, date), and a Mac path never reaches a Linux session.
- A salience gate before write: 45% less junk stored, no recall lost in 18 runs.
- A benchmark you can re-run: over 90 simulated days, 23-62x fewer context tokens than sending the whole history, same or better recall on personal facts.
Where it still loses, published: on customer-support dialogue at 30 days recall was 0.25 until this week; the bug was my contradiction pass archiving a loyalty number for a vaguer restatement. It's 0.875 now, and the experiment log with the rejected results is in the repo (experiments/QUEUE.md, RESULTS.jsonl).
Install: pip install mengram-ai && mengram setup --key . Or no account at all: the checkpoint and the task card work locally. Apache-2.
Question for you: what do you lose most between sessions, the state, the decisions, or the reasons? That's what I'll build next.
About Mengram 2.47 on Product Hunt
“Memory that survives /clear, compaction and the next agent”
Mengram 2.47 was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #101 on the daily leaderboard. Seven months after the first launch, this one is about the work, not memory types. Hooks for Claude Code, Codex and Cursor: recall on every prompt, save after each turn, a checkpoint before compaction that puts the working state back. New: `mengram resume`, a task card (done, remaining, last test and its commit) the next session or agent picks up, keyed by repo and branch. Every fact says which tool wrote it and when. Benchmark: 23-62x fewer context tokens than sending history, losses published.
On the analytics side, Mengram 2.47 competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Mengram 2.47 performed against the three products that launched closest to it on the same day.
Who hunted Mengram 2.47?
Mengram 2.47 was hunted by Ali Baizhanov. 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 Mengram 2.47 including community comment highlights and product details, visit the product overview.
Ali Baizhanov