SaaS Metrics
Product Positioning & Context
PMB gives Claude Code, Cursor, Codex and Zed persistent project memory through MCP. It stores decisions, lessons, goals, recent work, project facts and docs in one SQLite workspace on your disk. No cloud, no API keys, no LLM call on the read path. It is open source, offline-first, inspectable/exportable, with a local dashboard and honest impact tracking so you can see which memories actually help.
Related Ecosystem & Alternatives
Discover adjacent products, open-source repositories, and developer tools sharing similar technical architecture.
Deep-Dive FAQs
What is PMB?
PMB is a digital product or tool described as: Stop re-explaining your project to AI coding agents
Where did PMB originate?
Data for PMB was aggregated directly from the Product Hunt community ecosystem, representing raw developer and early-adopter sentiment.
When was PMB publicly launched?
The initial public indexing or launch date for PMB within our tracked developer communities was recorded on June 29, 2026.
How popular is PMB?
PMB has achieved measurable traction, logging over 189 traction score and facilitating 49 recorded discussions or engagements.
Which technical categories define PMB?
Based on metadata extraction, PMB is categorized under topics such as: Open Source, Developer Tools, Artificial Intelligence.
Is PMB recognized by media or academic researchers?
Yes. It has been covered by media outlets like Nature.com. This indicates the concept has reached a level of mainstream or scientific viability beyond just developer forums.
Are there open-source alternatives related to PMB?
Yes, the GitHub ecosystem contains correlated projects. For example, a repository named phuryn/pm-skills shares highly similar architectural descriptions and topics.
How does the creator describe PMB?
The original author or development team describes the product as follows: "PMB gives Claude Code, Cursor, Codex and Zed persistent project memory through MCP. It stores decisions, lessons, goals, recent work, project facts and docs in one SQLite workspace on your disk. No..."
Community Voice & Feedback
So if I have a session in Claude, it will have the memory to store the chat from claude and when I switch to chatgpt or others llms it wil pick up the left over work from claude?
the "no cloud, on your disk" call is the whole thing for me — local-first genuinely changes what people will put in their memory. building healthos on the same constraint. how's retrieval holding up as the graph grows into thousands of entities?
Congrats on shipping! What is next on the roadmap after launch day?
Love the local-first SQLite approach here; keeping project memory on-disk instead of a hosted service is a smart trust boundary, because sensitive architecture decisions and lessons learned never leave your machine.
Everything staying right here on my own machine is the part that lands for me, Oleksii. Repeating myself over and over has quietly been one of my least favorite parts of the day, so this feels like a real relief.
Keeping agent memory in one local SQLite file is a clean approach. Re-explaining project decisions across Claude Code, Cursor, and Codex gets old fast, so shared MCP memory could make coding sessions feel much less repetitive.
Persistent memory for coding agents is one of those features where “what not to remember” matters as much as what to store.The part I’d be most curious to see is a small memory diff after each session: new lesson added, old assumption updated, and which memory actually influenced a suggestion.That would make it easier to trust local memory instead of treating it like a hidden second prompt. Also helps catch stale project decisions before an agent keeps repeating them.
How do you decide what gets written into memory, like is it automatic from chats or only explicit saves?
But remembering also has a cost. Your front loading context. aka using a lot of the available context before solving a problem. That means your runway to solve it is smaller. How do you get around that? some times you need all the context runway you have 😅
the "no LLM call on the read" part is a nice detail. most memory solutions make an API call every time the agent needs context, which adds latency and cost to every single interaction. storing it in local SQLite and letting the agent pull what it needs without a round trip makes way more sense for coding workflows where speed matters. does it handle memory conflicts though? like when two sessions produce contradicting decisions about the same part of the codebase.
This is exactly the problem that makes AI coding feel like a conversation reset every 5 minutes. You paste context, it forgets, you paste again. The local-first memory angle is smart - keeping it in the project rather than some cloud sync feels like the right call for sensitive codebases. Does it handle monorepos where different agents might need different context scopes?
We run agents across multiple client projects simultaneously, so stale memory leaking into the wrong context is a real operational risk. The keyed fact system handling latest-wins with old value archived covers simple attribute updates, but I'm curious how it handles decisions that don't have a clean key (for exmaple, an architectural direction that got reversed mid-project without an explicit "we switched from X to Y"). Does the conflict surface in the dashboard, or does the old decision just keep scoring well on BM25 until someone manually archives it?
Boring demo video could have been much much better. Fix it if you want to onboard more customers! Still good product so upvoting :)
PMB’s local SQLite + no read-path LLM claim is the part I’d test first. The hard bit with project memory is not storing more facts; it’s deciding when an old fact should lose authority.Do you track expiry or conflict per memory item? For example, if a repo switches from REST to GraphQL, I’d want the old REST decision preserved as history but not injected into a fresh coding-agent context unless the current file still touches that path. The dashboard would be more useful if it shows not just “this memory helped”, but “this memory was skipped because it conflicted with newer evidence.”
@oleksiijko - Very nice. Definitely beats managing multiple .md files locally with skill integration. Will review in more detail, but looks very promising.
Discovery Source
Product Hunt Aggregated via automated community intelligence tracking.
Tech Stack Dependencies
No direct open-source NPM package mentions detected in the product documentation.
Media Tractions & Mentions
Deep Research & Science
No direct peer-reviewed scientific literature matched with this product's architecture.