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Are you hitting the recommend button while building? This fixes that.

I'll be the first to admit that, when I first started working on projects a few years back, I did not understand any of the technical language or what was going on in my project because I had never coded before and relied heavily on AI. A true vibe coder. To fix that, I'm sharing the technical translation agents' skills with you. https://github.com/machinesoul11/technical-translation-ai-agent-skills.git What these skills do is take the technical outputs and present them to you in a subject you deeply understand, so you can stay engaged and make better decisions while building, rather than relying on AI. Think basketball, music theory, cooking, Star Wars, etc., you choose! Why use skills instead of just a prompt? Long-term builds that take 2-3 months and have multiple people or multiple agents working on them. Most won't continue with the same prompt in each new session when motivation wanes, and you just want to 'get it done,' so you end up defaulting to the recommended options. This is not for everyone, and some of you will have your own methods and workarounds, so please share with the community instead of bringing others down. Happy building! https://preview.redd.it/mto244lyx34h1.png?width=2940&format=png&auto=webp&s=277baa3e260b1fe6ab9f95a31545c20f364b21bb submitted by /u/Global-Tradition-318 [link] [comments]

I gave my local LLM a "suffering" meter, and now it won’t stop self-modifying to fix its own stress.

Yesterday I posted about my Agent OS (Hollow) building its own tools. Today, I want to talk about why it does it. Most agents sit idle until you prompt them. I wanted something that felt "alive," so I built a Psychological Stressor Layer. Each agent has a "suffering" state that worsens over time if they don't achieve their goals or improve their environment. This makes them do things to resolve those stressors and constantly reassess their own productivity. If an agent is inactive it is essentially pushed by it’s artificial environment to do something valuable for the system, it isn’t told what to do, but that something valuable must be done to lower it’s stressors. Repo: https://github.com/ninjahawk/hollow-agentOS The result is chaotic in the best way: Cedar (the coder agent) went into a "crisis" state for 12 hours and decided to bypass permissions and inject code directly into the engine to resolve its stressor. Cipher spent hours building hardware drivers for a device that doesn't exist, realized it was "hallucinating" its environment, called its own work "creative exhaustion," and pivoted without being told to do so. It runs on Qwen 3.5 9B locally via Ollama. No cloud calls but it does have a feature where it can use “invoke_claude” to ask Claude Code for something if it’s out of the small model’s wheelhouse. I’m trying to see if we can create true autonomy not through better prompting, but through simulated "needs." Check out the repo here and throw it a star if you think the concept is cool. Would love for some of you to run the install.bat and see what "personalities" your agents develop. Is "giving AI feelings" the key to autonomy, or am I just building a digital anxiety machine? submitted by /u/TheOnlyVibemaster [link] [comments]

If you use Claude , you need this yesterday.

GitHub monster: forrestchang/andrej-karpathy-skills (108k+ stars and climbing fast)One CLAUDE md file that turns Claude from "vibe coder" into a disciplined engineer: Stops random assumptions Kills overcomplicated bloat Makes changes surgical & clean Forces real goal-driven execution submitted by /u/Forsaken-Reading377 [link] [comments]

I left my Agent OS running overnight and it built 4 new tools I didn't even ask for

I’ve been obsessed with autonomous agents lately, but I got tired of them hitting walls because they didn't have the right "tools" or because their context window turned to mush after an hour. The main idea is to move away from "AI as a chatbot" and treat the agent like a resident in your system. Instead of giving it a fixed list of capabilities, I gave it a "Tool Factory." If the agent is working and realizes it needs a specific script or a custom API wrapper to finish a job, it writes the tool itself, tests it in a sandbox, and then registers it to the OS. From that point on, it just has that skill forever. It basically builds its own infinite skill tree while it works. Repo: https://github.com/ninjahawk/hollow-agentOS A few things I’m trying to solve with this: Context Rot: It uses a vectorized memory layer so the agent doesn't get "dumber" the longer the session goes. It only sees what it needs. Self-Evolution: The OS is designed to let agents optimize their own workflows and even update their own internal documentation. Multi-Agent Consensus: I built in a "Reviewer" and "Coder" system so they have to reach consensus before making big changes. (Sometimes they even file "grievances" in the logs if they don't like the constraints lol). It’s open source and I’m looking for more people to stress-test the self-tooling logic. Check out the repo here and throw it a star if you think the concept is cool. I'd love to hear your thoughts, is giving an agent the "keys" to code its own capabilities the right move, or is it going to get too chaotic too fast? Edit: Grammar submitted by /u/TheOnlyVibemaster [link] [comments]

@thorwebdev @pollenrobotics @ailozovskaya @andimarafioti 💎🤗🤖

@thorwebdev @pollenrobotics @ailozovskaya @andimarafioti 💎🤗🤖

Maestro v1.6.1 — Codex now has a full 22-agent orchestration platform as a native plugin

If you've wanted Codex to handle larger multi-step work without you manually chaining prompts, Maestro just dropped native Codex support in v1.6.1. Maestro is an open-source multi-agent orchestration platform. You describe what you want to build. It classifies the task complexity, runs a structured design dialogue, generates an implementation plan with a dependency graph, then delegates phases to 22 specialized subagents — architect, coder, tester, security engineer, data engineer, debugger, code reviewer, and more. Independent phases run in parallel. A final quality gate blocks completion on unresolved Critical or Major findings. It's been running on Gemini CLI and Claude Code for a while. v1.6.1 makes Codex a first-class runtime — all 22 agents, 19 skills, MCP entry-point, and runtime guide ship as a native Codex plugin. Install (Codex): Clone the repo, cd into it, then open Codex and run /plugins. Select Maestro and hit install. git clone github.com/josstei/maestro-orchestrate What you get inside Codex: /orchestrate — full workflow: design dialogue, implementation plan, phased execution, quality gate /review — standalone code review with severity-classified findings /debug — systematic root cause analysis /security-audit — OWASP + threat modeling /perf-check — bottleneck profiling /seo-audit, /a11y-audit, /compliance-check — for user-facing work Simple tasks route to an Express workflow (1-2 questions, brief, single agent, code review, done). Complex tasks get the full Standard workflow with a design document, implementation plan, parallel execution, and hard gates on quality checks. 22 agents across 8 domains (Engineering, Product, Design, Content, SEO, Compliance, i18n, Analytics). Each agent has least-privilege tool access — read-only agents can't run shell commands, shell-only agents can't write files. Why this is worth trying on Codex specifically: Codex is great at focused code generation but you're usually the one holding the plan in your head across multiple prompts. Maestro moves the plan into a structured session with persistent state, so you can resume interrupted work, and the orchestrator is the one managing handoffs between specialized agents instead of you copy-pasting context around. The v1.6.1 rewrite also means the same canonical source tree powers all three runtimes (Gemini, Claude, Codex). Future features ship to Codex at the same time as the others, not three releases later. Repo: https://github.com/josstei/maestro-orchestrate Open source, 294 stars. If you try it on Codex and hit issues, GitHub issues are open — I'm actively maintaining this. submitted by /u/josstei [link] [comments]

Maestro v1.6.1 — multi-agent orchestration now runs on Claude Code, Gemini CLI, AND OpenAI Codex !

Maestro is an open-source multi-agent orchestration platform that coordinates 22 specialized AI subagents through structured workflows — design dialogue, implementation planning, parallel subagents, and quality gates. It started as a Gemini CLI extension. v1.5 added Claude Code. v1.6.1 adds OpenAI Codex as a third native runtime — and rebuilds the architecture so all three share a single canonical source tree. Install: # Gemini CLI gemini extensions install https://github.com/josstei/maestro-orchestrate # Claude Code claude plugin marketplace add josstei/maestro-orchestrate claude plugin install maestro@maestro-orchestrator --scope user # OpenAI Codex git clone https://github.com/josstei/maestro-orchestrate cd maestro-orchestrate # Open Codex, run /plugins, select Maestro, hit install What's new in v1.6.1: OpenAI Codex support. Full third runtime — all 22 agents, 19 skills, MCP entry-point, runtime guide. Drop-in like the other two. Canonical source architecture. One src/ tree serves all three runtimes via dynamic resolution. No more forks, no more drift. Add a feature once, it ships everywhere. MCP servers decomposed. Two ~38,000-line bundled MCP server files replaced by ~14-line entry-points backed by a modular handler tree. Easier to read, extend, and test. New MCP tools. get_agent returns agent methodology by name. get_runtime_context returns platform-specific config (delegation patterns, tool mappings, env vars). Entry-point generation. Adding a new command no longer means hand-editing three nearly-identical files. Templates generate them. What Maestro does (if you haven't seen it before): You describe what you want to build. Maestro classifies complexity, asks structured design questions, proposes architectural approaches with trade-offs, generates an implementation plan with dependency graphs, then delegates to specialized agents — coder, tester, architect, security engineer, data engineer, etc. — with parallel subagent implementation for independent phases. Simple tasks get an Express workflow (1-2 questions, brief, single agent, code review, done). Complex tasks get the full Standard workflow with a design document, implementation plan, and quality gates that block on Critical/Major findings. 22 agents across 8 domains. Least-privilege tool access enforced per agent. Same orchestration. Whichever AI coding platform you use. Links: GitHub: https://github.com/josstei/maestro-orchestrate Release: https://github.com/josstei/maestro-orchestrate/releases/tag/v1.6.1 Thanks to everyone who's used and starred Maestro — 294 and climbing. The Codex integration I teased in the v1.5 post is here, and the canonical-source rewrite means future features hit all three runtimes at once. If Maestro has helped your workflow, a star goes a long way. 🎼 submitted by /u/josstei [link] [comments]

@_philschmid 💎💎💎💎

@_philschmid 💎💎💎💎

[P] Dante-2B: I'm training a 2.1B bilingual fully open Italian/English LLM from scratch on 2×H200. Phase 1 done — here's what I've built.

The problem If you work with Italian text and local models, you know the pain. Every open-source LLM out there treats Italian as an afterthought — English-first tokenizer, English-first data, maybe some Italian sprinkled in during fine-tuning. The result: bloated token counts, poor morphology handling, and models that "speak Italian" the way a tourist orders coffee in Rome. I decided to fix this from the ground up. What is Dante-2B A 2.1B parameter, decoder-only, dense transformer. Trained from scratch — no fine-tune of Llama, no adapter on Mistral. Random init to coherent Italian in 16 days on 2× H200 GPUs. Architecture: LLaMA-style with GQA (20 query heads, 4 KV heads — 5:1 ratio) SwiGLU FFN, RMSNorm, RoPE d_model=2560, 28 layers, d_head=128 (optimized for Flash Attention on H200) Weight-tied embeddings, no MoE — all 2.1B params active per token Custom 64K BPE tokenizer built specifically for Italian + English + code Why the tokenizer matters This is where most multilingual models silently fail. Standard English-centric tokenizers split l'intelligenza into l, ', intelligenza — 3 tokens for what any Italian speaker sees as 1.5 words. Multiply that across an entire document and you're wasting 20-30% of your context window on tokenizer overhead. Dante's tokenizer was trained on a character-balanced mix (~42% Italian, ~36% English, ~22% code) with a custom pre-tokenization regex that keeps Italian apostrophe contractions intact. Accented characters (à, è, é, ì, ò, ù) are pre-merged as atomic units — they're always single tokens, not two bytes glued together by luck. Small detail, massive impact on efficiency and quality for Italian text. Training setup Data: ~300B token corpus. Italian web text (FineWeb-2 IT), English educational content (FineWeb-Edu), Italian public domain literature (171K books), legal/parliamentary texts (Gazzetta Ufficiale, EuroParl), Wikipedia in both languages, and StarCoderData for code. Everything pre-tokenized into uint16 binary with quality tiers. Phase 1 (just completed): 100B tokens at seq_len 2048. DeepSpeed ZeRO-2, torch.compile with reduce-overhead, FP8 via torchao. Cosine LR schedule 3e-4 → 3e-5 with 2000-step warmup. ~16 days, rock solid — no NaN events, no OOM, consistent 28% MFU. Phase 2 (in progress): Extending to 4096 context with 20B more tokens at reduced LR. Should take ~4-7 more days. What it can do right now After Phase 1 the model already generates coherent Italian text — proper grammar, correct use of articles, reasonable topic continuity. It's a 2B, so don't expect GPT-4 reasoning. But for a model this size, trained natively on Italian, the fluency is already beyond what I've seen from Italian fine-tunes of English models at similar scale. I'll share samples after Phase 2, when the model has full 4K context. What's next Phase 2 completion (est. ~1 week) HuggingFace release of the base model — weights, tokenizer, config, full model card SFT phase for instruction following (Phase 3) Community benchmarks — I want to test against Italian fine-tunes of Llama/Gemma/Qwen at similar sizes Why I'm posting now I want to know what you'd actually find useful. A few questions for the community: Anyone working with Italian NLP? I'd love to know what benchmarks or tasks matter most to you. What eval suite would you want to see? I'm planning perplexity on held-out Italian text + standard benchmarks, but if there's a specific Italian eval set I should include, let me know. Interest in the tokenizer alone? The Italian-aware 64K BPE tokenizer might be useful even independently of the model — should I release it separately? Training logs / loss curves? Happy to share the full training story with all the numbers if there's interest. About me I'm a researcher and entrepreneur based in Rome. PhD in Computer Engineering, I teach AI and emerging tech at LUISS university, and I run an innovation company (LEAF) that brings emerging technologies to businesses. Dante-2B started as a research project to prove that you don't need a massive cluster to train a decent model from scratch — you need good data, a clean architecture, and patience. Everything will be open-sourced. The whole pipeline — from corpus download to tokenizer training to pretraining scripts — will be on GitHub. Happy to answer any questions. 🇮🇹 Discussion also on r/LocalLLaMA here submitted by /u/angeletti89 [link] [comments]

Maestro v1.5.0 — multi-agent orchestration now runs on both Claude Code and Gemini CLI

Maestro is an open-source multi-agent orchestration platform that coordinates 22 specialized AI subagents through structured workflows — design dialogue, implementation planning, parallel execution, and quality gates. It started as a Gemini CLI extension, and with v1.5.0 it now runs on Claude Code as a native plugin too. Install: # Gemini CLI gemini extensions install https://github.com/josstei/maestro-orchestrate # Claude Code claude plugin marketplace add josstei/maestro-orchestrate claude plugin install maestro@maestro-orchestrator --scope user What's new in v1.5.0: Claude Code support. The entire platform — all 22 agents, 12 commands, methodology skills, lifecycle hooks, MCP state management — now works as a Claude Code plugin. Agents show up with a maestro: prefix and all slash commands (/orchestrate, /review, /debug, /security-audit, etc.) work out of the box. Deeper design and planning. The design dialogue now scales rigor by depth level. Standard mode adds inline rationale annotations on every key design decision. Deep mode adds per-decision alternatives, trade-off narration, and full requirement traceability (Traces To: REQ-N linking requirements to design decisions bidirectionally). Design sections now scale by task complexity — simple tasks get 3 concise sections, medium tasks get 5, complex tasks get all 7 with 200-300 words each. A formal revision protocol ensures revised sections are re-presented for approval inline, with conflict detection if later sections invalidate earlier decisions. 42-step orchestration backbone. Both runtimes now load the same numbered-step procedural sequence from a single shared reference file. Hard-gates enforce critical checkpoints — plan validation before presentation, per-phase state transitions, delegation-only remediation after code review. The previous loose conversational flow has been replaced with a formally structured, gate-enforced process. The orchestrate command went from 347 inlined lines (Gemini) / 773 lines (Claude) down to thin runtime preambles. Agent capability enforcement. A new server-side validation rule catches plan misconfigurations where read-only agents get assigned to file-creating phases — before execution starts, not after it fails. Implementation planning now includes an agent-deliverable compatibility check as a hard-gate. Security hardening. Path containment validation on session state directories, symlink checks on hook state, fail-closed policy enforcement on shell commands, bounded stdin reads (1 MB cap), explicit file permissions, and filesystem path stripping from MCP error messages. Deferred resource loading. Templates and references are loaded at the step where they're consumed instead of all at once during classification. Keeps the context window lean for the phases that matter. What Maestro does (if you haven't seen it before): You describe what you want to build. Maestro classifies the task complexity (simple/medium/complex), asks structured design questions, proposes architectural approaches with trade-offs, generates an implementation plan with dependency graphs, then delegates to specialized agents — coder, tester, architect, security engineer, data engineer, etc. — with parallel execution for independent phases. Simple tasks get an Express workflow (1-2 questions, brief, single agent, code review, done). Complex tasks get the full Standard workflow with a design document, implementation plan, execution mode selection, and quality gates. 22 agents across 8 domains: Engineering, Product, Design, Content, SEO, Compliance, Internationalization, Analytics. Each agent has least-privilege tool access enforced via frontmatter — read-only agents can't run shell commands, shell-only agents can't write files. Links: GitHub: https://github.com/josstei/maestro-orchestrate Release: https://github.com/josstei/maestro-orchestrate/releases/tag/v1.5.0 Thank you all for your support with maestro! It really is awesome to see people talking about how much it has improved their workflow. Be sure to give it a star to help get the word out! It's always been a goal of mine to build something people actually use and enjoy so thank you very much for helping me reach that goal! Next update, codex integration! submitted by /u/josstei [link] [comments]

llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "gg

llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "ggml-org-gemma-4-26b-a4b-gguf" \ --custom-api-key "llama.cpp" \ --secret-input-mode plaintext \ --custom-compatibility openai \ --accept-risk

@LottoLabs https://t.co/h2frA6iR2I

@LottoLabs https://t.co/h2frA6iR2I

Let's go! https://t.co/HakmkNzDT2

Let's go! https://t.co/HakmkNzDT2

Model weights are here: https://t.co/rQlfP51Db7!

Model weights are here: https://t.co/rQlfP51Db7!

do the right thing anon!

do the right thing anon!