Jinnkunn

Ph.D. Student 🇨🇦🇨🇳 Research encompassing LLM Reasoning, XAI, and Visualization.

[Cloudflare @Cloudflare]

Today we're rolling out Temporary Accounts on Cloudflare Workers. Any agent can now run wrangler deploy — temporary and get a live Worker in seconds. https://t.co/o5GLomVUxb

[Jinnkunn @_jinnkunn]

这个海上的雾就很奇妙

[MiniMax (official) @MiniMax_AI]

MiniMax M3, Open-Weight, Now On Hugging Face , with only ~428B parameters and ~23B activated parameters

Weights:
https://t.co/g4Ybfa2kWH
MiniMax Sparse Attention:
https://t.co/HcTlWRotG3

[Jinnkunn @_jinnkunn]

等等… 再说一遍这支股票代码叫啥？

[Jinnkunn @_jinnkunn]

感谢学生 🥺🥺🥺

[Jinnkunn @_jinnkunn]

@arkuy99 @okooo5km 我也一样

[ClaudeDevs @ClaudeDevs]

Claude Fable 5 is here. New model generation, new way of working.

Here's how to get started in Claude Code and on the Claude Platform: 🧵

[Jinnkunn @_jinnkunn]

@Kkarftisaboy 我的心岛吗？

[Jinnkunn @_jinnkunn]

@youjiang1120 悠老师贴贴

[Jinnkunn @_jinnkunn]

小茂密

[Jinnkunn @_jinnkunn]

Codex 是会比喻的

[Jinnkunn @_jinnkunn]

@arkuy99 @strrlthedev 还有一个额外的副作用，可能会有焦虑… 如果本身就有焦虑的话则会更严重。我现在彻底戒了咖啡（咖啡因也会引起和加剧焦虑心悸 hhh

[Jinnkunn @_jinnkunn]

@arkuy99 @strrlthedev 困的话可以再加一个 Clonidine 可乐定。我也是吃了 vyvanse 后贼精神睡不着，医生给开了可乐定

[Jinnkunn @_jinnkunn]

@arkuy99 Microsoft E5 Developer 带的域名邮箱是我用过免费的里面最好用的

[Jinnkunn @_jinnkunn]

我已经，很久都不开 vscode... 需要编辑器的地方只用 Zed

[恒星 @vintcessun]

RUBRIC-ARROW 提出了一种交替式 rubric 奖励建模框架，专门解决非可验证场景下 LLM 后训练的评分问题。

本质上，它把原本依赖布尔聚合导致频繁平局的硬评分，换成概率打分 + 交替 GRPO 训练，让点式评判器只靠成对偏好数据就能学会按标准打分，不再需要前沿模型助攻。

实验显示奖励建模精度和下游对齐效果都有提升。感觉主观生成领域的后训练，可能会因为这个方向变务实。

https://t.co/mTITabuAGJ

[Muratcan Koylan @muratcan]

Gradient descent for SKILL.md files sounds interesting, maybe a bit complex but it's becoming a real part of agent harness.

SkillOpt is one of the first papers to treat markdown skill files as trainable parameters and provides a proper optimization framework for them.

A few things I learned that you should consider too.

1. The validation gate is the only thing that matters in a self-editing loop.

Held-out set, strict improvement, ties rejected. End-to-end, their best skills land with 1 to 4 accepted edits total. If your "self-improving agent" is accepting most of what it proposes, you're shipping slop.

2. Bounded edits are better than full rewrites. 4 to 8 edits per step is the sweet spot.

Remove the budget and performance collapses. This is the textual analog of learning rate, and it transfers to any LLM-as-author loop. If you're using an agent to refactor your docs, your prompts, or your skills, cap the diff size.

3. Compactness wins. Median final skill: ~920 tokens.

Skills do not need to be long. They need to be high-signal. Most skill files I see are bloated because length feels like effort. It isn't.

4. The harness is becoming less important; the skill is becoming more important.

A Codex-trained skill ported into Claude Code hit +59.7 points on SpreadsheetBench. Procedural knowledge is more general than the runtime that
produced it.

5. Frozen model + trained context is the practical adaptation.

GPT-5.4-nano with a SkillOpt'd skill ≈ frontier behavior on procedural benchmarks. Cheaper, portable, inspectable, zero inference-time cost. This is
the answer to "how do we adapt a frontier model for our domain" for almost everyone who isn't training their own models.

6. Verification is the bottleneck.

Every gate in this paper depends on an auto-grader. That works for benchmarks. It fails for writing, design, and strategy, exactly the open-ended work we want to automate. Whoever builds the verifier for open-ended tasks owns the next stage.

There are also two leassons I learned while shipping v2.3.0 of my Context Engineering Agent Skills repo, measured across composer-2, claude-opus-4-7,
gpt-5.5, and gemini-3.1-pro via the @cursor_ai SDK:
- Description and body are two different surfaces. The router only sees the description. The agent sees the body once activated. They can quietly disagree, and only end-to-end task tests catch it.
- Aggregate accuracy is the wrong unit. When I rewrote three descriptions, the corpus average moved ~1pp. Individual skills moved 23–25pp. Per-skill effect size is where the action is.

Also, in Feb 2026 I shared a piece called Personal Brain OS arguing that the markdown file is a first-class substrate for agent state. SkillOpt is the optimizer-shaped version of that same argument: not "store memory in files" but "treat files as trainable parameters with proper optimization machinery around them." That's the move from static to measured.

The fast/slow split they describe already lives implicitly in the digital-brain-skill repo:
- voice-guide and tone-of-voice.md are slow-state (rarely touched)
- posts.jsonl and bookmarks.jsonl are fast-state

What SkillOpt adds that I didn't have is a protected section invariant, a structural guarantee that fast edits cannot overwrite slow lessons. Removing that mechanism cost them 22 points on SpreadsheetBench. Worth borrowing.

If you're building agents, SkillOpt: Executive Strategy for Self-Evolving Agent Skills is a good paper to read: https://t.co/ZS9SZXQ6Mv

[Jinnkunn @_jinnkunn]

为什么 Claude 这么喜欢说一些不着边际的话

[Jinnkunn @_jinnkunn]

谁？我吗？

[Jinnkunn @_jinnkunn]

腾讯企业邮箱设计的... 真的很难用... 特别是跟企业微信融合之后