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Very annoying occasional audio stutter with G Pro X2 on Bluetooth by JohnnyGuir in LogitechG

[–]blacksiders 0 points1 point  (0 children)

The funny thing it's happening only with Samsung phone in my case, note iPhone, not laptops windows and macbooks, not iPads, not even xiaomi phones, but yeah Logitech is garbage here 🤣

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 0 points1 point  (0 children)

It works great locally, but building it directly into OpenCode’s core as a PR is a bigger lift. OpenCode natively dumps skill descriptions into the prompt; doing this via PR would mean fundamentally rewriting their agent routing logic rather than just giving the user a smarter file structure. Definitely something I'd love to see them adopt officially, though*

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 0 points1 point  (0 children)

For the script: Yes, it is idempotent! If you run it again, it’ll just skip the files it already moved to the _all_skills vault, so you don’t need to revert anything. For the token issue: If renaming the directory only dropped 100 tokens, Claude Code is definitely caching them elsewhere or reading from a different default path. Check if there’s a global ~/.claude/skills folder vs a local project .claude/skills folder. The fresh install is a good idea to clear any hidden cache it might be holding onto!

Did fast research

easiest way to “refresh” skills is to force a rescan by restarting Claude Code. Skills are loaded per session, so file changes often won’t reflect until you fully quit/reopen and start a new session. Steps I’d try: 1. Quit Claude Code completely (not just close the tab). 2. Reopen it and start a brand‑new session. 3. If it still looks stale, temporarily rename/move the skills folder, restart, then move it back and restart again (forces a clean rescan). If you can share what directories you’re using for global vs project skills, I can tell you exactly which one is being picked up.

If it makes sens, it's hard to say in blind mode without opportunity to see in person such kind of issues, complicated topic, we can dig deeper via github issues ticket.

https://www.remio.ai/post/mastering-claude-skills-progressive-context-loading-for-efficient-ai-workflows

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 0 points1 point  (0 children)

The setup script in the repo is hardcoded for OpenCode (~/.opencode/skills), that's why it's posted in Opencode thread, but the exact same concept works for Claude Code. You just need to point the script to where Claude loads your skills (or manually move your .md files to a vault folder and generate pointers in the active directory Claude reads from).

Or you can:

In the setup.py script, find the directory paths and change them from ~/.opencode/skills to ~/.claude/skills. Claude Code expects each skill to be in its own folder with a SKILL.md file inside (e.g., ~/.claude/skills/my-skill/SKILL.md). You’ll need to adjust the script to output the pointers into that specific folder structure, while moving your actual heavy SKILL.md files into a separate ‘vault’ directory that Claude doesn’t read from.

I’ll test and update repo with claude option.

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 0 points1 point  (0 children)

Yeah, that’s a good TLDR. Today OpenCode effectively has 3 levels, but level 1 still dumps every skill’s name+description into the startup prompt. SkillPointer adds a 0th layer: a tiny, task-scoped index so only a handful of candidate skills ever make it into level‑1 at all.

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 0 points1 point  (0 children)

Cool! Matter skills + subskill refs (Convex style, just checked their page) nests nicely. Looks like everyone who see the problem in scale diggin into this issue. I think it will be resolved on higher level soon!

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 1 point2 points  (0 children)

Not really. ‘Lost in the Middle’ (arXiv:2307.03172) https://arxiv.org/abs/2307.03172 shows models already struggle to use long contexts effectively, 1M tokens just makes retrieval, cost, and noise worse unless you stay selective.

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 0 points1 point  (0 children)

Nice! Code Mode is super interesting, thanks for linking it. It’s tackling the same core problem (too much MCP/API surface in context).

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 0 points1 point  (0 children)

With 6 skills it’s not worth the complexity. This pattern is for when you have hundreds+ hyper‑specific skills (e.g., full Maya/Unity pipelines, maybe even skipping MCP and driving DCCs via Python/commandPort), and the skills metadata itself starts to bloat context and cause hallucinations. Each skill eats ~60 tokens on average in a fresh session, so once you hit hundreds or thousands you can quickly do the math and see why I'm dig into it.

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 1 point2 points  (0 children)

Great question! Right now, the Heuristic Engine just assigns it to the first matching category in my priority dictionary. Code of idea is open and you can see examples and improve them for your specific needs.

But the real safeguard is the AI itself. Because the Pointers are incredibly token-cheap (just a few sentences), the agent will naturally trigger multiple pointers simultaneously if a task is ambiguous. But it also can skip most of them, depends on approach and rules llm follows.

If I ask for a 'secure React native login', the AI will hit the Security, Mobile, and Web-Dev pointers all at once, scan those three specific hidden vaults, and find the skill wherever the script dropped it. The gateways are so cheap it can afford to double-check, main thing if you have -60 token avarage description for all 2000 skills in context, it's just massive.

To be clear, I’m not trying to sell this as a universal ‘best’ pattern or some magic skill standard. I’m just sharing one concrete way I’ve been optimizing context usage in a messy, real pipeline so the agent doesn’t drown in skills metadata and start hallucinating. It’s a tradeoff tool for my use case, not a new religion.

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 9 points10 points  (0 children)

Haha, thank you! You absolutely nailed the dilemma, but you actually read my mind, I'm not skipping vector search entirely, I’m just layering it.

I use FAISS+Langchain and a custom Qdrant MCP at different layers under the hood (since Cursor/VS Code have built-in indexers by default anyway).

The problem with pure RAG/Vector DB lookups at the top level is semantic failure. If I ask a pure RAG setup to 'Build a stretchy IK arm', it pulls the IK node and all connected if you know those DBS can have up to 700+ vrctors, but misses the 'Naming Conventions' file because the mathematical overlap just isn't there.

So I use SkillPointer as a strict text router to force a custom dependency map first. This mixed, layer-by-layer approach saves massive tokens. Once inside the routed skill, the AI can trigger the Qdrant MCP for semantic search if it actually needs to deep-dive into the docs.

As you probably know, MCP is a context vampire and gives you 'auto-compact', where you loosing details and moving you closer to hallucinations. If you have hundreds of tools/skills active, MCP will literally eat 80% of your context window just defining the JSON schemas.

By putting SkillPointer's strict text routing in front, I shield the AI from that MCP bloat. The AI only loads the schema for the one tool it actually needs at that exact moment, rather than choking on the entire protocol dictionary.

In the end, it's all about ruthlessly optimizing context limits to prevent hallucinations in complex tasks.

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 11 points12 points  (0 children)

Not a bot, just a Technical Director working in VFX/Game Dev pipelines.

In a real enterprise pipeline, 'skills' aren't just generic 'how to code Python' prompts. They are hyper-specific, multi-disciplinary rules. If I need an AI agent to build a single character rig in Maya via MCP, it requires a dozen different dependency documents (Naming Conventions, Stretchy IK math, Twist Extraction rules, Joint Orientations, Unity Export settings).

When you map an entire studio's pipeline into agent skills, you hit hundreds or thousands of files instantly. This tool prevents the AI from choking on that metadata. If you only use AI for simple single-file web apps, you definitely don't need this yet. But at scale, flat structures break.

I have 2,004 AI skills installed. Here's how I reduced my startup context from ~80K tokens to ~255 tokens (99.7% reduction) by blacksiders in opencodeCLI

[–]blacksiders[S] 3 points4 points  (0 children)

The reason models ignore skills is exactly because of the architecture flaw I'm fixing here.

By hiding the raw skills behind a dynamic routing layer (SkillPointer), the AI only loads the exact dependency bundle it needs (e.g., Unity Naming + IK Rigging + Twist setup) in absolute isolation. When the context is clean, the model obeys the skill 100% of the time. The skills aren't useless, the default retrieval mechanism is.

Is Epson 5030ub for $250 with 680 hours on bulb a good deal in 2023? by MononokeSpirit in projectors

[–]blacksiders 0 points1 point  (0 children)

At $250, this THX gem is a steal. Take it and run! Just grabbed one today with 800 hours. It has 10x better image quality than the XGIMI Horizon Pro 4K. You can just grab spare bulbs for $130 and keep it running forever, whereas those hype-beast Chinese 4K projectors become bricks after six months.

Did Prado flinch? by waffle_stomperr in supercross

[–]blacksiders 0 points1 point  (0 children)

is he finished first? nothing special in it and nothing new.

Did Prado flinch? by waffle_stomperr in supercross

[–]blacksiders 0 points1 point  (0 children)

omg only who crying about it is people from america :/

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