2,166 karma · joined January 5, 2017
As per the announcement, Gemini 3.1 Pro score 68.5% on Terminal-Bench 2.0, which makes it the top performer on the Terminus 2 harness [1]. That harness is a "neutral agent scaffold," built by researchers at Terminal-Bench to compare different LLMs in the same standardized setup (same tools, prompts, etc.).
It's also taken top model place on both the Intelligence Index & Coding Index of Artificial Analysis [2], but on their Agentic Index, it's still lagging behind Opus 4.6, GLM-5, Sonnet 4.6, and GPT-5.2.
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[1] https://www.tbench.ai/leaderboard/terminal-bench/2.0?agents=...
In retrospect, I'd probably avoid "useless." While it's a fairly descriptive term for their finding, it probably carries a subjective tone.
I might have been a bit blunt with the title - sorry about that, but I still think it was a good title. From what I've observed, a lot of Skills on GitHub are just AI-generated without any feedback or deliberative refinement. Many thought those would still be valuable, but you've shown evidence otherwise.
I chose a title that directly reflects an interesting finding - something that offers substantial insight to the community. I think the rule should be applied with some nuance; in this case, being explicit is a net positive.
I have no interest in linkbait, and I hope that's evident from my previous submissions
- Minimal, configurable context - including system prompts [2]
- Minimal and extensible tools; for example, todo tasks extension [3]
- No built-in MCP support; extensions exist [4]. I'd rather use mcporter [5]
Full control over context is a high-leverage capability. If you're aware of the many limitations of context on performance (in-context retrieval limits [6], context rot [7], contextual drift [8], etc.), you'd truly appreciate Pi lets you fine-tune the WHOLE context for optimal performance.
It's clearly not for everyone, but I can see how powerful it can be.
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[1] https://lucumr.pocoo.org/2026/1/31/pi/
[2] https://github.com/badlogic/pi-mono/tree/main/packages/codin...
[3] https://github.com/mitsuhiko/agent-stuff/blob/main/pi-extens...
[4] https://github.com/nicobailon/pi-mcp-adapter
[5] https://github.com/steipete/mcporter
[6] https://github.com/gkamradt/LLMTest_NeedleInAHaystack
Traditional systems (git blame, review history, ticket links) tell you who committed or approved changes, but not whether the content originated from an AI agent, which model it used, or what prompt/context produced it.
Agent Trace is aiming to standardize that missing layer so different tools can read/write it consistently.
You definitely want to build that habit when you're at your best.
I use both interchangeably.
> Large Language Model usage levels out the Dunning–Kruger effect.
That's basically my title. I think that's the interesting finding in the study.
> Comet isn't available for your system yet. Comet is currently available for Windows and macOS.
The real underrated skill is realizing there isn't one.
[1] https://www.seangoedecke.com/ai-is-good-news-for-australian-...
I'd guesstimate a 2× increase in their operational complexity. So, if they previously required 0.5 DevOps of a full-timer, they'll now need one more DevOps full-timer just to handle the added complexity.
Does that make sense to you?
- The length of tasks AI can complete doubles every ~7 months
- In 2-4 years, AIs could autonomously complete week-long projects.
- In under 10 years, they might handle month-long software or knowledge work.
[1] https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
Looks like a very fresh business [1]; it only became worth writing about once they were ready to pitch their product.
The overall point stands, yet the specific "73% fake traffic" figure should be taken with caution until independent data backs it up.
I got pretty close with one, but I wasn't a good match for the other two. Compared to direct applications, HN had the best SNR for me.
I'm based in the Middle East, so only companies that hire globally would consider me. If I were based in the US or EU, I would've definitely had more.
And now, my data is open-source ಠ_ಠ
Here's a sample of what I’m currently following - maybe it'll help you build your own:
- Job listing (WWR [1], RemoteOK, etc.), because I'm looking for a job.
- Tech news: HN ≥ 100 pts [2], Github trends [3], TLDR AI & webdev [4], and r/technology [4], Product Hunt [6], Google devs [7].
- Science: r/science [4], ScienceDaily [8], and a couple few more.
- IMDb latest shows [4]: Sci-fi, Adventure, etc.
- And a lot of blogs!
RSS feeds are sometimes hidden - this tool can help: https://lighthouseapp.io/tools/feed-finder
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[1] https://weworkremotely.com/categories/remote-back-end-progra...
[2] https://hnrss.org/newest?points=100
[3] https://raw.githubusercontent.com/mhadidg/gh-trends/refs/hea...
[4] Using RSSible [5] - a small utility I built, turning webpages into an RSS feed.
[5] https://rssible.hadid.dev/
[6] https://www.producthunt.com/feed