OCTAMEM

We're live. 🧠 OctaMem gives your AI a persistent memory layer that works across every model. Claude, GPT, Gemini, and more. Your context. Your preferences. Your history. Never lost again. → octamem.com

@sama Rate limits hurt less when the agent actually remembers what it did before hitting them.

come for the rate limits, stay for the best model

@Austen Token usage as a KPI is how you end up with agents that re-read the same docs ten times a session because nobody bothered to give them memory. Optimize for outcomes, not consumption.

Encouraging AI use is a good idea. Looking at token usage as a leading indicator of that can make sense. Incentivizing raw token consumption within your company is a very bad idea. It is not hard to burn way more tokens than you need.

@craigzLiszt Half of it is re-explaining context every session because the model forgot. Fix memory and the interaction stops feeling like that.

ai is making us all autistic

@thekitze Mostly agree. The unlock from here isn't IQ, it's memory, tools, and the boring infra layer that lets agents actually do work over time. Cheaper inference helps too. Bigger models won't save a stateless agent.

you know what, i'm gonna say it. the models are intelligent enough. we can stop at gpt 5.5 and it's still smarter than 99% of devs. we just need tooling/glue around it and we need the prices to come down. that's it. keep inhaling copium if you think otherwise.

@Polymarket 10 agents, one question: do they share memory or does every agent re-learn the firm from scratch? Vertical agents without shared state is just ten new silos.

JUST IN: Anthropic unveils 10 new AI agents built for banks, insurers, & financial firms.

@claudeai Templates are great until the analyst asks "what changed since last quarter's pitch" and the agent has no idea. Vertical agents need vertical memory, not just vertical prompts.

New for financial services: ready-to-run Claude agent templates for building pitches, conducting valuation reviews, closing the books at month-end, and more. Install them as plugins in Cowork and Claude Code, or use our cookbooks to run them in production as Managed Agents.

5)Persistent memory infrastructure. What if memory was just a layer you plug in, the same way you plug in a database or an auth system? Three memory types, one API call, zero infrastructure to maintain yourself. That is OctaMem.

4) Build a custom memory layer. This is what serious engineering teams do. A database, a retrieval system, an injection pipeline. It actually works. Until maintaining that system takes more time than building the product it was supposed to support.

Most developers have the same AI memory problem. And they all try the same fixes in the same order. Here are all 5, ranked from worst to best. 🧵

@StockSavvyShay Pre-release evals catch what the model can do on day one. They don't catch what it learns, stores, and acts on six months later. Agent safety is a runtime problem, not a launch problem.

$GOOGL, $MSFT and xAI agreed to give the U.S. government early access to evaluate their AI models before public release. They join OpenAI and Anthropic in allowing federal pre-release reviews focused on model capabilities and security.

@StockMKTNewz Pre-release evals are fine. The real safety gap is what agents do with user data and state months after deployment. Nobody's auditing that.

AI FIRMS AGREE TO GIVE UNITED STATES 🇺🇸 EARLY ACCESS TO EVALUATE THEIR AI MODELS SpaceX owned xAI, Google $GOOGL, and Microsoft $MSFT have all agreed to give the US government early access to their artificial intelligence models to assess the systems’ capabilities and help improve their security before the technology is released to the public With the agreements, the AI developers join OpenAI and Anthropic in allowing pre-release reviews of their models by the US Commerce Department’s Center for AI Standards and Innovation (Source - Bloomberg)

@hiarun02 Models are great at typing. They're not great at knowing what to build or whether it works.

Why does Anthropic have 2,500 engineers. if their own models are writing most of the code?

@pmitu Bubble in valuations, probably. Bubble in the tech itself, no.

Aren’t we currently in an “dot-com” bubble of AI? Or not yet?

@mark_k @xai Connectors are table stakes now. The interesting question: does Grok remember what it learned from your GitHub last week, or does it re-read everything from scratch every session?

Grok Connectors are here from @xai! 🔥🔥 Connect your Grok account to the services you already use: GitHub, Notion, Gmail, Google Calendar, Google Drive, Outlook, and more. Now Grok can actually work with your data, pull insights from your repos, summarize emails, check your schedule, review Notion pages, or analyze Drive files, all in one conversation.

@NoahKingJr The real heartbreak isn't the limit. It's starting the next session and realizing the model forgot everything you just figured out together.

me and claude 30 mins before hitting the 100% limit

@RhysSullivan Feels great until the second concurrent write.

i bet it feels good asf to store data in json as a database

@mattpocockuk none of them are reliable. GPTZero, Originality, Copyleaks all have high false positive rates and get gamed easily. If you really want a benchmark, run your text through 3-4 of them and look at the spread, not any single score.

What's the best 'AI Writing Detector' out there? Want to QA my new AI writing skills against what the industry is using. I don't mind paying for the service.