kamer

Building causal-memory: agent memory with a causal core, in Rust. Agents that remember why they failed. Also: quant multi-agent systems + MCP.

[kamer @kamerFo]

@yifanxu_ephai 这是干嘛的 文件系统？

[kamer @kamerFo]

> Neither, exactly. The unit is a decision → outcome edge.
>
> A decision can span many tool calls, and an observation is just evidence. What gets stored is: what the agent chose, in what context, what it observed afterwards, and the relation (caused / enabled / prevented / no_effect), plus where the confidence came from.
>
> Edges link through "influenced_by", so you can trace multi-hop chains back to the root decision, or query "what did similar actions cause" before acting.

[kamer @kamerFo]

I'm a software engineer based in China, with 4 years at a major internet company working on big data and agent observability. 🇨🇳

Now building in the open: causal-memory (agent memory with a causal core, in Rust), quant multi-agent systems + MCP.

Looking to connect with:

• Solo founders
• Software engineers
• Indie hackers
• AI / agent builders
• Product builders
• People building cool stuff

Open to remote work and interesting collaborations.

Let's connect, learn, and build together. 🤝

[kamer @kamerFo]

Alpha Desk is live 🚀
Giving an AI agent market data is easy now. Plug in a data MCP, add a few skills, and it can "do research".
But that agent only got a pair of eyes. The numbers in its report are still whatever the model says they are. It forgets everything when the chat ends. And when people call it "self-improving", it usually means the model rewrote its own prompt because it felt better about it.
I spent 4 years building big data and agent observability systems at a large internet company. Alpha Desk is what I think an AI research desk should actually look like.
It's a preconfigured A-share research workspace for DeepSeek Harness: 9 finance bots, 35 research skills and 6 report types, ready out of the box.
📊 Numbers come from code, not from the model
The report engine runs fetch, compute, render, then independent review. The bot that writes a report never reviews it. Every number in the final report traces back to the data and the calculation that produced it.
🧠 Research compounds instead of resetting
Deep memory records what each bot concluded, why, and how it turned out, so cause and outcome stay linked. A shared team knowledge base keeps all 9 bots working from the same facts, with independent review and source drift detection when an upstream source changes.
👥 A team, not a do-everything assistant
9 bots with clear roles, 35 skills and 6 report types (technical comparison, stock snapshot, event note, earnings review, sector comparison and deep-dive), already wired together, so you don't have to design the workflow or write the prompts yourself.
🧬 Agent self-evolution (RSI, in development)
This isn't a skill that edits its own prompt. RSI evolves the whole 9-bot team, and every change has to earn its place:
Bots rerun on frozen data snapshots, so results are comparable.
Scoring has three layers: outcome 40%, process 45%, cost 15%.
Each experiment changes exactly one file, as a single-variable A/B test.
A change is promoted only with n ≥ 10 and the lower bound of the 95% confidence interval above zero.
A human reviews and merges it. Nothing merges itself, and every change can be rolled back.
Most "self-improving" agents just tell you they got better. Ours have to prove it with experiments, then get a human sign-off.
Early access is open now 👉 https://t.co/50s0nUdJGX
Research only. Not investment advice.

[kamer @kamerFo]

@ValueSelfiy 我把你包皮割下来 塞你妈嘴里

[kamer @kamerFo]

@Sevgiyldrm010 我把你奶子割下来 塞你尿道里

[kamer @kamerFo]

@kavgasal 我把你奶子割下来 塞你阴道里

[kamer @kamerFo]

@FireworksAI_HQ 牛逼

[kamer @kamerFo]

@xtony1314 腰斩又腰斩咋���

[kamer @kamerFo]

@s4blGlAV0OhnCRZ @wangyunWY0203 臭虫 被日本殖民的时候生出来的臭虫

[kamer @kamerFo]

@_ninetyhe 我准备做的 哈哈哈🤣

[kamer @kamerFo]

DeepSeek Harness now supports native memory plugins, and causal-memory already runs as one.

Your DSH agent remembers not just what happened, but why: decisions, their outcomes, and the bad actions it was stopped from taking.

https://t.co/OCleD1RteJ

#DeepSeek #AIAgents

[kamer @kamerFo]

Built Alpha Desk on @Fei2411's DS Bot: one install, a 9-bot A-share research team.

Chief sets up the team. Chan theory overlays on the chart, and agents only mark levels they can back with data. Price alerts wake a specific bot. No buy/sell calls.

https://t.co/OCleD1RteJ

[kamer @kamerFo]

@itsMarioJJ 么么哒💋

[kamer @kamerFo]

causal-memory loses to mem0 on fact recall. LoCoMo: 79.1% vs 91.6%, and that's in the README on purpose. Fact recall isn't what it's for. On CausalEval, which tests why things happened, it scores 81% vs mem0's 65%, and 70% vs 40% on "if I do X again, what happens?"

[kamer @kamerFo]

@yupi996 哈哈哈🤣

[kamer @kamerFo]

@Reci00007 来了

[kamer @kamerFo]

@popoonexu 我是搞ai的 互关一下吧

[kamer @kamerFo]

@AItd986 Mai

[kamer @kamerFo]

@NPCqzp 来互关 兄弟们