Plus, the model got trained and RLed with a continuous context, except if they now tune it with messing with the context as well.
https://manus.im/blog/Context-Engineering-for-AI-Agents-Less...
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Plus, the model got trained and RLed with a continuous context, except if they now tune it with messing with the context as well.
https://manus.im/blog/Context-Engineering-for-AI-Agents-Less...
Edited version: We try to solve a similar problem to put long documents in context. We built an MCP for Claude to allow you to put long PDFs in your context window that go beyond the context limits: https://pageindex.ai/mcp .
Just a heads-up: HN folks value transparency, so mentioning if it's yours usually builds more trust.
Why are both this new Memory API and the Filesystem as (evolving) Context releases only for the Developer API - but not integrated into Claude Code?
1. Multi-agent orchestration 2. Summarising and chunking large tool and agent responses 3. Passing large context objects by reference between agents and tools
Two things to note that might be interesting to the community:
Firstly, when managing context, I recommend adding some evals to our context management flow, so you can measure effectiveness as you add improvements and changes.
For example, our evals will measure the impact of using Anthropics memory over time. Thus allowing our team to make a better informed decisions on that tools to use with our agents.
Secondly, there's a tradeoff not mentioned in this article: speed vs. accuracy. Faster summarisation (or 'compaction') comes at a cost of accuracy. If you want good compaction, it can be slow. Depending on the use case, you should adjust your compaction strategy accordingly. For example, (forgive my major generalisation), for consumer facing products speed is usually preferred over a bump in accuracy. However, in business accuracy is generally preferred over speed.
For context: I have background in CV and ML in general. Currently reviewing and revising RL.
Any idea how I can get into RL?
I have 3 years of industry/research experience.
Whenever I see post like this, it triggers a massive fomo creating a scene of urgency on I should work in these problems.
Not being able to work here is making be anxious.
what does it take for someone in Non-US/Non-EU region to get into big labs such as these?
Do I really have to pursue PhD? I am already old that pursuing PhD is a huge burden that I can't afford.
I want to really get into anthropic. For context: I have background in CV and ML in general. Currently reviewing and revising RL. Any idea how I can get into RL? I have 3 years of industry/research experience. Whenever I see post like this, it triggers a massive fomo creating a scene of urgency on I should work in these problems. Not being able to work here is making be anxious. what does it take for someone in Non-U…
I want to really get into anthropic. For context: I have background in CV and ML in general. Currently reviewing and revising RL. Any idea how I can get into RL? I have 3 years of industry/research experience. Whenever I see post like this, it triggers a massive fomo creating a scene of urgency on I should work in these problems. Not being able to work here is making be anxious. what does it take for someone in Non-U…
The pace is so fast, if you have FOMO you've already missed out most probably. If you're interested in LLM flavored RL, I'd suggest prime-rl (and their discord) community, hugging face RL courses with smol (you'll need pro and burn a couple of bucks), etc. etc.
Really need to get hands dirty here. I remember taking RL course from coursera during 2020 covid. I didn't have the chance to apply it in the problems I worked post covid.
But I really want to start doing RL again. Interested in world models and simulation for RL.
Nice. When using OpenAI Codex CLI, I find the /compact command very useful for large tasks. In a way it's similar to the context editing tool. Maybe I can ask it to use a dedicated directory to simulate the memory tool.