Steven (Batman) Batchelor-Manning @S_BatMan
Founder | CTO | Building Exceptional Technology linkedin.com/in/sbatman/ Bratislava, Slovakia Joined September 2010-
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@wong2__ There are so many good agentic memory systems out there, so picking the right one is critical x.com/i/status/20505…
Good to see further improvements to a powerhouse model
🔥 MiniMax-M3 just got faster. Thanks for all the excitement around MiniMax-M3 — the response has been far beyond our expectations. Last night, we rolled out a major inference upgrade: 🛠️ Fixed an issue that could occasionally produce abnormal tokens 💾 Increased memory and
@sakurayukiai if i go back to the very first article of the series `rag is not enough` and the more apporaches taken like this that stop hope dumping into the context the better.
Your memory system should not be deciding what the agent sees. The agent should. New Article on the quiet reversal in agent memory: stop injecting context, start giving the agent tools. Live now.
@PenfieldLabs graph is a powerhouse but as most systems have found maintining the graph at scale is either slow, hard, costly or all three. looking forward to seeing what the future brings in that space
@leetllm most teams i work with the first thing i do is get them using a tool recalled system, and they all see the same.
The piece also covers the two-step rhythm (search returns previews, a separate call fetches the full record, ~200K tokens saved per session), and the oh-my-kiro observation pattern. x.com/S_BatMan/statu…
The highest-leverage refinement in the whole piece: make every tool response end with one line about what to call next. No documentation. The agent learns the API through use. The trace becomes self-documenting.
Here it is, the long awaited MiniMax M3.
Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 34.8% SWE-fficiency, 28.8% KernelBench Hard, 74.2% MCP Atlas - MiniMax Sparse Attention scales context to 1M -
Getting the right Agentic memory saves you tokens, limits, costs and a headache, Get it right!
Hows that context looking for you ? Because there is a good chance you are wasting a lot of tokens x.com/S_BatMan/statu…
@garrytan I reviewed 19 memory systems and im pretty such each of you didnt pick the best for you, but take a look and see if you nailed it x.com/S_BatMan/statu…
@AzFlin I'd go for a middle-ground, a trusted curated set of skills community managed with safety gates (looking at you npm) and then harnesses that are able to acquire skills on demand if they dont already have something better Matrix style, learn to kungfu
Rag is where most agentic memory systems begun, some layered on it, some added along side it, others ripped it out entirely, but one thing is for certain Vector RAG is not enough x.com/S_BatMan/statu…
@FM_yassin If you think this is good, you should see what some of the approaches can offer you, hindsight specifically : x.com/S_BatMan/statu…
@nateherk A good Agentic Memory solution is the quick way to start saving tokens and doing more with less x.com/S_BatMan/statu…
A super neat article for compression methods and optimisation Bookmark it and definitely give it a read !
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