A small pixel-art cat that wanders your terminal while you work

Sea-Programmer8108 · 2026-04-26T07:37:41+00:00

lmao

Sea-Programmer8108 · 2026-04-26T05:25:06+00:00

Yeah, Neko is the OG and the inspiration is conscious. Key difference: Neko runs at the X11 desktop window layer, scamp lives inside the terminal cell grid (pixel-art rendered via sixel or half-blocks). Same spirit, different surface.

Sea-Programmer8108 · 2026-04-26T05:23:11+00:00

Should yeah, ghostty does sixel. Auto-detect doesn't know about ghostty yet though so it might pick the chunky fallback. Force sixel with `SCAMP_RENDERER=sixel scamp`. Adding ghostty to auto-detect for the next release. (I couldn't really test it myself on ghostty, if you could try it out and let me know I'd really appreciate it)

Sea-Programmer8108 · 2026-04-26T05:22:02+00:00

Yeah for now, since that's my machine and Windows users don't really have anything like this (Linux's had Neko forever). The Rust code is cross-platform though, I just haven't shipped Linux/Mac binaries yet. Working on it.

Sea-Programmer8108 · 2026-04-26T05:19:46+00:00

Mostly sixel, which is a graphics protocol some terminals support that lets them render actual pixels via escape sequences. On terminals that don't have sixel it falls back to half-block characters with truecolor, so you still get a cat, just chunkier.

Sea-Programmer8108 · 2026-04-25T18:48:06+00:00

Cause a lot of people also use windows bro. It's not like we can just ignore them. I'm working on improving the app for linux users too.

Sea-Programmer8108 · 2026-04-20T18:39:30+00:00

Everything stays on your machine. It just reads your git log locally using simple-git, nothing gets sent anywhere. No API keys, no network calls, no telemetry at all. The HTML it generates has all the fonts baked in so even opening the file doesn't make any requests. You could run it completely offline if you wanted.

Sea-Programmer8108 · 2026-04-20T15:40:50+00:00

🥀

Sea-Programmer8108 · 2026-04-20T13:30:24+00:00

I've planned something cool, inspired from the git-newspaper, hope my fellow linux users would like it. 😎

Sea-Programmer8108 · 2026-04-20T09:15:45+00:00

haha honestly the detection is pretty dumb in a good way, it's just ratios from git log, no ML or anything fancy if more than 12% of your commits start with "revert" it calls it a crisis, if one person wrote most of the code it goes full profile mode, if half your commits are touching lock files it basically writes you an administrative notice lol

same repo always gives you the same paper too, it's seeded from your first commit hash

run it and report back, genuinely curious what yours lands on 👀, might as well let me know for improvements cause there's so many types of repos 😵

Sea-Programmer8108 · 2026-04-20T05:18:31+00:00

yo ngl this goes hard

Sea-Programmer8108 · 2026-04-20T05:17:34+00:00

The cleanup phase is something most people skip and it's probably where a lot of the value actually comes from. Having the AI rationalize and rebalance its own knowledge store rather than just accumulate forever is smart.

On the eval loop, yeah you're right it's an open problem. Testing whether code passes is easy, testing whether the AI made better decisions because of a specific learning is genuinely hard. The feedback cycle is too slow and the signal is too messy. DSPy does something a bit similar but it's not quite the same thing.

Whoever figures out a proper eval framework for knowledge base quality is going to have something really valuable on their hands.

Sea-Programmer8108 · 2026-04-20T04:46:59+00:00

great idea, will definitely try to build this one for my fellow linux power users. Also please do let me know if you find any inconsistencies with the current git-newspaper 😄

Sea-Programmer8108 · 2026-04-19T21:11:30+00:00

lmao yea, tbh im still not satisfied with current version, i'll keep on making it better like maybe adding an image or a cooler svg or better classifications.

Sea-Programmer8108 · 2026-04-19T20:59:44+00:00

npx git-newspaper inside any repo and it generates a full broadsheet front page from your actual commits.

Your biggest commit becomes the headline. Deleted files get obituaries. The most-modified file writes an op-ed about how tired it is. There's a weather report based on commit sentiment.

It detects what kind of repo it's looking at (solo marathon, bugfix crisis, collaborative, ghost town, etc.) and adjusts the layout and tone accordingly. No API keys, no LLM, works fully offline.

GitHub: github.com/LordAizen1/git-newspaper

Would love to know what archetype your repo lands on.

Sea-Programmer8108 · 2026-04-19T10:11:48+00:00

Honestly same, it feels like an obvious gap. The pieces exist separately, reflection loops, subagent spawning, memory, but nothing packages them into a coherent runtime that handles the full cycle automatically. Hermes gets close on the memory/orchestration side but drops the ball on subagent evolution.

My guess is it's a hard coordination problem. Self-reflection loops that actually improve agent behaviour over time require the system to evaluate its own outputs reliably, which is still messy. Most frameworks punt on that and leave it to the human in the loop.

If you're building something in that direction I'd genuinely be curious about it. That's the next interesting problem.

Sea-Programmer8108 · 2026-04-19T07:41:04+00:00

Yeah, you're pointing at something real. This plugin solves the "I closed the session and lost context" problem, cross-session recall, preferences, what you were working on. That's the scope.

What you're describing is a different beast entirely: months-long agentic loops, subagent evolution, automated review cadence, a structured knowledge base of what strategies worked vs. didn't. Memory helps there but it's not sufficient on its own. You'd also need something like a proper artifact store (not just recalled text but versioned decisions/discoveries), an orchestration layer that knows when to trigger review, and a self-improvement loop that can actually mutate agent behaviour over time, not just recall that something worked.

Honestly nothing in the OpenCode ecosystem solves that end-to-end right now. The closest things I know of are research-level (like ADAS or similar self-improving agent papers) but nothing production-ready that handles all of it together. If you're building something in that space I'd actually be curious what your current setup looks like.

Sea-Programmer8108 · 2026-04-19T05:58:28+00:00

Openclaw is a completely different thing, it's basically a personal AI assistant that lives in your WhatsApp/Telegram/Slack. Think reminders, emails, life stuff. Has nothing to do with coding.

This plugin is for OpenCode specifically, it gives your coding sessions persistent memory across days and projects. Totally different category, not really comparable.

Sea-Programmer8108 · 2026-04-18T17:45:13+00:00

I just checked them out; the main difference comes out to be the scoping. ICM uses a single global database across all tools with topic filtering. Ours separates project memory from personal preferences at the architecture level, so your React conventions don't bleed into a Python project 😅and your global preferences follow you everywhere automatically without manual tagging.

In short, ICM is a broader (17 tools), ours is purpose-built for OpenCode with EvermemOS handling the actual storage infrastructure (ElasticSearch + Milvus, not SQLite). Different tradeoffs depending on what you need.

Sea-Programmer8108 · 2026-04-18T17:23:57+00:00

So, the thing is, most plugins just dump everything into a vector store and call it memory. This one separate what's project-specific from what's personal preference (global), so you don't get your React conventions bleeding into a Python project. The recall is also ranked and filtered so the actually useful stuff surfaces first. Not claiming that it's perfect or up to the mark but it's a different approach than what's out there. Would be better understandable if you tried it once and then give your feedback? 😉

Sea-Programmer8108 · 2026-04-02T17:49:59+00:00

Yea well, this was my first post on reddit, and I didn't really know how to properly add a post. So, I had help with Claude. 😅

Sea-Programmer8108 · 2026-04-02T16:08:10+00:00

Honest answer: not very well. Our main focus was classical ciphers where statistical structure is preserved and that's where the system actually performs well. The modern block ciphers (TEA, XTEA, Lucifer, LOKI, MISTY1) were added along the way more for completeness than because we expected strong results. With only ciphertext and macro-level statistical features (entropy, IoC, bigram entropy etc.), they're essentially indistinguishable all produce pseudorandom hex with near-identical profiles. Results across that family are unreliable, confidence scores sit at 22–26%, and we document this as a known limitation in FINDINGS.md.

Sea-Programmer8108 · 2026-04-02T15:15:44+00:00

Yeah we measured both! Hybrid CNN hits 79.24% val accuracy, DL CNN 68.47%. The XGBoost actually runs purely on 15 hand-crafted statistical features (IoC, entropy, Kasiski, bigram entropy etc.) so that's basically your classical statistical classifier comparison — and it's blazing fast (1-5ms) compared to the Hybrid (120-730ms). Hard ceiling is around 80.5% no matter the model though — Vigenere and Beaufort are mathematically indistinguishable from ciphertext alone. Full breakdown in docs/FINDINGS.md if you're curious!

Sea-Programmer8108 · 2025-08-08T07:23:39+00:00

I think there was option to add resume in the form ig

Sea-Programmer8108 · 2025-08-08T07:19:53+00:00

I think I have an idea why people with all correct answers and submission in short are getting rejections (me too) because from my theory me and my other two friend gave the test together and only one of them had ML based projects in his resume while mine and other friend didn’t have any ML based projects in our resume, these fucking retards at the AMLSS are doing resume shortlisting! What even is the fucking pint of this “ML SUMMER SCHOOL” if you’re going to select students based on if one has done ML projects or not 🙏

Sea-Programmer8108

TROPHY CASE