Chris Latimer

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Chris Latimer

Chris Latimer

@chrislatimer

Co-founder @Vectorizeio. Working on Hindsight https://t.co/wzeEbFpCFl

Boulder, CO Katılım Mart 2009
377 Takip Edilen925 Takipçiler
Chris Latimer retweetledi
Vectorize
Vectorize@Vectorizeio·
Hindsight 0.8.3 is out! You can now preview exactly what a piece of content would remember before you store a single thing. Plus OCR, sharper retrieval, and a data-integrity fix that matters if you ingest large docs. Breakdown below 🧵
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Chris Latimer retweetledi
Vectorize
Vectorize@Vectorizeio·
We are ranked in the top 10 fastest growing Open Source orgs according to @supabase and @commitvc! Ranked across 40,000+ open source GitHub orgs we are thrilled to be one of the fastest growing organizations! Huge thanks to everyone building with Hindsight, opening issues, and starring the repo!
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Chris Latimer retweetledi
Vectorize
Vectorize@Vectorizeio·
How We Built a 4-Way Hybrid Search System That Actually Runs in Parallel. When we were designing Hindsight's memory retrieval, we had to confront one of the hardest challenges with AI data retrieval. "Parallel" async code that wasn't actually parallel. You write four nice async functions, sprinkle in some awaits, and end up executing everything one after another. For a hybrid search stack with multiple retrieval strategies, that's unacceptable. Here's how we built a 4-way hybrid search system that really does run in parallel, how we evolved it to share connections and reduce round-trips, and how reranking ties it all together. Spoiler: the biggest bottleneck turned out not to be query speed — it was connection pool contention, and that reshaped the whole architecture. Read more: linkedin.com/pulse/how-we-b…
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Nicolò Boschi
Nicolò Boschi@nicoloboschi·
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Andrew Levine
Andrew Levine@andrarchy·
Don’t use MemPalace by Milla Jovovich, use Hindsight by @Vectorizeio
Andrew Levine@andrarchy

I love the idea that Milla Jovovich is getting into open source development with her AI memory solution MemPalace and I was ALMOST going to integrate it into my OpenClaw... But here's why I didn't and why I went with a little known piece of software named Hindsight by @Vectorizeio (though with 7.8k stars on github). What I liked about MemPalace was the idea of really efficiently compressing all my conversations with my agents so that they can "remember everything" (long term memory) and then basically injecting an even more compressed version of that memory back into my agent's context window so that my agent always "knows everything" (active memory) without costing me a fortune and eating all my context up. MemPalace aced the marketing of this concept... But unfortunately they didn't nail the execution. I'm not saying that Jovovich is being dishonest in how she promoting this software. It's open source, I don't see an indication they intend to monetize it and I don't really see a path to that anyway (as I will explain next). I think two friends had an idea and they figured out how to execute on it and then they promoted it in a way that felt honest to them. I actually think it's great that a celebrity is promoting open source software development and she deserves a lot of credit for that. I also thing that this is a natural consequence of the democratization of software development that is happening as a result of AI. The number of people creating repos and submitting pull requests is going to grow exponentially and I love that. The problem? There's just better solutions out there. I'm already running one named lossless-claw (github.com/martian-engine…) and that works great. But lossless-claw is a plugin for OpenClaw, I want something that works with my other AI tools as well. I bounce around Codex, Claude Code, and other AI tools all the time and each new session feels like I'm starting from scratch. That's especially the case now that Anthropic has banned OpenClaw. What I REALLY want is a way of SYNCING this knowledge base across my agents so that they all know everything I'm working on, but none of the open source solutions I could find do that. I believe there are some paid ones (maybe @supermemory or @ByteroverDev ) but I'd prefer to explore a self-hosted/OSS solution before introducing a paid dependency into my system. For me, the promise of these open source AI agents is giving everyone real ownership over artificial super intelligence, and the more monthly subscription fees I have to pay to make my assistant function, the less real that feels to me. Doesn't mean I won't pay them, just means I want to minimize them. Hindsight checks all my boxes. It ships with official, first-party plugins/hooks for OpenClaw, Codex, and Claude Code. All three tools point to the same central memory bank. I just set one shared bankId and my agents should all be synced. Every conversation I have in Codex should get automatically compressed, reflected on, and made available in Claude Code (and vice versa). Hindsight is fully open-source (MIT), runs 100% locally if I want, uses my existing LLMs for the background reflection/extraction, and the whole thing is lightweight to operate. It's the right solution for what I'm trying to build which is open source software that turns OpenClaw and Hermes Agent into a real life Jarvis. Long term memory is an important piece of that puzzle and right now, Hindsight seems like the best fit. @mem0ai and @Letta_AI are both strong open-source memory solutions with flexible APIs and good multi-agent capabilities, but they’re general-purpose tools that would have forced me to build a bunch of custom integration work just to get shared memory working across Codex CLI, Claude Code, and OpenClaw. Hindsight has the foundation I need to enable my agents to stay perfectly in sync AND it significantly outperforms both on the major memory benchmarks, hitting 91.4% on LongMemEval compared to Mem0’s 49%, without fudging the numbers like MemPalace.

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Chris Latimer retweetledi
Vectorize
Vectorize@Vectorizeio·
Our biggest update yet, Hindsight 0.5.0 is here! - Constellation view - Bank templates hub (including templates for @claudeai code / Codex coding assistants and for @NousResearch Hermes Agent and @openclaw personal personal assistants and others) - Built-in llama.cpp LLM provider for fully local inference support - And much more!
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Aimar Haddadi
Aimar Haddadi@AdvicebyAimar·
i can spot a grifter from miles away. so i digged into the code to figure out if this is legit or not. guess i was right. ben is a crypto founder who runs some weird bitcoin lending platform, i was pretty sure he knows absolutely nothing about ai and memory so i tracked down the repo myself since i was curious. his website says he likes to build ai powered products and train local ai models? sure man, 80% of your github repo's are bitcoin related stuff. only one ai related project came up you forked in 2024. mempalace has 10k github stars, more than 1k forks but only.. 7 commits ? apparently the best memory layer to date? no git author history, no account connected to whoever wrote the code of this codebase. it doesn't add up.. the account who pushed the original repo, named: aya-thekeeper, under aya-thekeeper/mempal got deleted right after the repo got published. you paid a random guy named lu to build this shit out for you. ( "Written by Lu (DTL) — March 24, 2026. For: Ben." ) - benchmark md file. lu wrote the code. lu wrote the benchmarks. lu is nowhere in the readme. or mentioned in the github history? the git history then got squashed to one commit and published under milla jovovich? seriously? a actress? you say she is a great friend of yours, she has been building this project with you. she does this at night. yet she has.. 7 commits and only 2 active days in her entire github history? you paid an actress and a random guy to promote a product you know absolutely nothing about.
Ben Sigman@bensig

30 second explanation of the MemPalace by Milla Jovovich. By day she’s filming action movies, walking Miu Miu fashion shows, and being a mom. By night she’s coding. She’s the most creative, brilliant, and hilarious person I know. I’m honored to be working with her on this project… more to come.

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Teknium 🪽
Teknium 🪽@Teknium·
Hermes Agent now supports @plastic_lab's Honcho, @mem0ai, @openvikingai, @Vectorizeio's Hindsight, @retaindb, and @ByteroverDev memory systems! Try them now with `hermes update` then `hermes memory setup` We have rehauled our memory system to be much more maintainable and pluggable, so anyone can make their own memory system to build on top of Hermes easily and cleanly with a special class of plugin! Which memory system is your favorite?
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Chris Latimer retweetledi
Vectorize
Vectorize@Vectorizeio·
One of the hardest problems we tackled in Hindsight was dealing with contradictions. When you're building a memory system for AI agents, reality isn't static. It evolves. A CRM agent might learn that "Acme Corp is a key prospect" in January, then encounter "Acme Corp is now a paying customer" in March. Naive approaches either lose the history or drown in duplicate facts. We needed a system that could handle this gracefully. Here's how we built it. hindsight.vectorize.io/blog/2026/02/0…
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Chris Latimer retweetledi
tahitimoon
tahitimoon@geek_bing·
I’ve tried a bunch of agent memory frameworks, and honestly, Hindsight just works better. mem0 and memobase don’t even come close. It’s super easy to get started. Just a few lines of code, and self-hosting is straightforward. No messy configs, no need to deal with a vector database. Everything is built on PostgreSQL. Memory, entities, relationships, and vectors all live in the same database. Compared to that, self-hosting mem0 and memobase is a pain. Feels like it’s designed that way to push people toward their cloud services. github.com/vectorize-io/h…
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Chris Latimer retweetledi
Vectorize
Vectorize@Vectorizeio·
Hindsight 0.4.20 is out. Check out our new @claudeai code, @LangChain's LangGraph, @nvidia's NemoClaw integrations and other improvements.
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Chris Latimer retweetledi
Vectorize
Vectorize@Vectorizeio·
Pydantic AI Persistent Memory: Add It in 5 Lines of Code! If you have built an AI agent with Pydantic ai, you already know it handles typed outputs, dependency injection, and async workflows well. But there is one thing it does not do: remember anything between runs. Every call to agent.run() starts with a blank slate. Your agent has no idea what the user said yesterday, what preferences they shared, or what it already researched. Adding Pydantic AI persistent memory does not require building a custom RAG pipeline or managing your own vector database. With the hindsight-pydantic-ai integration, you can wire long-term memory into any Pydantic AI agent in five lines of Python. This guide walks through the full setup, from installation to production-ready patterns. Read the complete walkthrough: x.com/Vectorizeio/st…
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Vectorize@Vectorizeio

x.com/i/article/2031…

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