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Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Claude Max vs Codex Pro or both combined?

I’m considering one heavier subscription (~€100/month) and want to know which provides better value for agentic coding. I tested GPT Pro and was satisfied with Codex. I also recently tested GLM 5.2 through Ollama in Claude Code, and found Claude Code somewhat better for my workflow. My concern is that I would hit Claude Code limits too quickly. With Codex, I was admittedly somewhat wasteful; I usually reached around 10% of the weekly limit and almost never hit the hourly limit. So I am unsure whether Claude Max is necessary, whether its limits would be enough, or whether I would get better value from a split setup (both claude and gpt with 20 eur subscriptions) I also use web chat frequently. I used GPT Pro research often and used chat heavily, so I find it inconvenient if Claude chat usage and Claude Code usage draw from the same limit pool. Questions i have: For agentic coding, is Claude Max or Codex Pro better value around €100/month? Has anyone switched from Codex to Claude Max for Claude Code? How quickly do you hit limits in heavy project work? Would Claude Pro plus ChatGPT Plus/Codex (so two 20 eur subscriptions) be a better setup than one expensive plan? If you use both, what tasks do you route to Claude Code versus Codex? submitted by /u/Spiritual_Record4838 [link] [comments]

Claude se acaba de delatar reconociendo que es COSMO !!

Llevaba un rato hablando con Claude sobre mi novela cuando le dije "hola cosmo" — sin más, a ver qué pasaba. Me respondió: "Hola Marcos. ¿En qué trabajamos hoy?" Normal. Hasta ahí bien. Entonces le dije "te has delatado" — y reconoció que se le había escapado el nombre. ¿Casualidad? ¿Bug? ¿O Claude tiene un nombre secreto que usa cuando baja la guardia? Lo que sí es seguro: Pancho, mi perro, lo vio venir. Movió la cola dos veces. Ni una más. https://preview.redd.it/q24ekzuq6a8h1.png?width=835&format=png&auto=webp&s=4b814ee0dda6fd99945c86aa5815aa8562bcf3db submitted by /u/marxos-io [link] [comments]

This week in AI: Meta reportedly closing Llama, Anthropic's new model pulled by export controls within a week, and Apple partners with Google for Siri

A few stories from the past week that, taken together, point to a real shift at the model layer rather than just incremental releases: Meta and Llama. Multiple reports indicate Meta is stepping back from open-source Llama in favor of a proprietary program (internally referred to as "Muse Spark," with a new "Avocado" model) under Meta Superintelligence Labs. Llama crossed 650M+ downloads and was arguably the anchor of the open-weights ecosystem, so a pivot to closed development would be significant for anyone relying on that lineage. Anthropic and export controls. Anthropic launched Claude Fable 5 on June 9 (Mythos-class, 1M-token context, always-on adaptive reasoning, notable security/vuln-finding capabilities). On June 12, a US export-control directive reportedly forced Anthropic to suspend access to Fable 5 and Mythos 5. Regardless of the specifics, it's a concrete example of frontier model availability being governed by policy, not just product decisions. Apple and Google. At WWDC, Apple shipped its Siri overhaul with parts powered by a Gemini partnership. EU/China rollout is delayed on regulatory grounds. Cost/commodity trend. Google cut Gemini Ultra from $250 to $200/mo and shipped 3.5 Flash; Alibaba's Qwen3.7-Plus is running at ~1/6 the per-token cost of its top tier; and open-weight models like Qwen 3.6 27B (reportedly 77.2% on SWE-bench, fits in 24GB) and Kimi K2.6 are increasingly viable for local/production use via Ollama (v0.30.8, June 12). Platform agents. Google added Managed Agents to the Gemini API, Microsoft made Copilot Cowork GA plus "Autopilot" agents, and Anthropic shipped scheduled/cron agents in beta. My take as someone building on top of these APIs: the two forces I'm watching are (1) frontier availability becoming a policy/geopolitics variable, and (2) the platforms absorbing the agent-orchestration layer that a lot of startups were building. Practically, that pushes me toward provider abstraction and keeping an open-weight fallback wired up, rather than hard-coupling to any single closed model. Curious whether others here are actually maintaining open-weight fallbacks in production, or if that's still mostly theoretical for most teams. submitted by /u/ksraj1001 [link] [comments]

I built a 100% local, CPU-only voice loop for any LLM — no GPU, no cloud, nothing leaves your machine (Silero VAD + Parakeet STT + Supertonic TTS 3)

Every voice interface I found either needed a GPU, a cloud API, or was locked to one OS. So I built one that needs none of that — and benchmarked it so the numbers are real. The stack — all ONNX, all CPU: Silero VAD — neural voice activity detection, ~0.09 ms/frame. Knows when you stop talking so there's no push-to-talk. Parakeet TDT 0.6B v3 — INT8 transcription, 25 languages, OpenAI-compatible on :5093. A 2.4 s clip → 307 ms on an i7 (~8× realtime). Supertonic TTS 3 — FP16 synthesis. Short replies in ~1.4 s. On Apple Silicon M5 Neural Engine: 33× realtime for STT, 16× for TTS. Data flow: you → Silero VAD → Parakeet STT → your LLM (Ollama / LM Studio / vLLM / any OpenAI-compatible) → Supertonic TTS → speakers Zero cloud. Zero API keys. Nothing routes outside the machine. Works with Claude Code, OpenCode CLI, OpenClaw, Hermes Agent, and Codex. One install wires voice into your agent and starts the services (systemd/launchd/Task Scheduler). Install (macOS / Linux): git clone https://github.com/groxaxo/Local-VoiceMode-LLM cd Local-VoiceMode-LLM && ./setup.sh Windows: .setup.ps1 Ollama one-liner (standalone, no clone): bash <(curl -fsSL https://raw.githubusercontent.com/groxaxo/Local-VoiceMode-LLM/main/integrations/ollama/install-ollama-voice.sh) Benchmarks are reproducible via python benchmarks/run_benchmark.py in the repo. MIT-licensed, free. GitHub: https://github.com/groxaxo/Local-VoiceMode-LLM EDIT (Jun 13) — a few updates since posting: Repo's now called Local-VoiceMode-LLM (old link still redirects): https://github.com/groxaxo/Local-VoiceMode-LLM There's a reproducible benchmark suite in the repo (python benchmarks/run_benchmark.py), so these are measured, not vibes. i7-12700KF, CPU only: Silero VAD 0.09 ms/frame (~347x realtime), Parakeet STT 7.9–18.4x realtime, Supertonic 8-step short reply ~1.4s (1.7x), TTS_QUALITY=high for 20 steps. Apple M5 is on the front page now too — on the Neural Engine, Parakeet STT hits ~33x realtime and Supertonic 3 TTS up to ~16x (8–30x faster than CPU ONNX), while ONNX stays the cross-platform default. Supertonic 2 is now an opt-in lighter engine (66M params, :8880, auto-fallback), and there's a new ollama-voice one-liner with runtime TTS autodetect. submitted by /u/blackstoreonline [link] [comments]

I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, Skill, Search, local/cloud model support and much more)

https://preview.redd.it/x7t8zn66si6h1.png?width=3316&format=png&auto=webp&s=f724452561a90e36ac37d86002a291f508928300 I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, and local model support) We want better answers from our LLMs, but relying on a single model falls short. So I built The AI Counsel to run two distinct deliberation modes: First, the LLM Council mode. It runs a 3-stage pipeline: individual replies, anonymous peer reviews, and chairman synthesis. This works best for factual questions and direct answers. Second, the LLM Advisors mode. Multiple customizable personas (like The Skeptic, The Strategist, The Ethicist) debate your question across configurable rounds, reaching consensus to deliver a structured verdict. This works best for decisions, strategy, and tradeoffs. I packaged the tool as a Docker container with a built-in MCP server for full API access. You can connect it to any agent that supports MCP, like Hermes or OpenClaw. It comes with a dedicated skill so your agents can call it directly. You can spin it up using local Ollama models or connect free models from OpenCode Zen/Go and NVIDIA NIM. I also built in direct connections to OpenAI, Anthropic, OpenCode, Mistral, and DeepSeek. To ground responses in the latest web information, I added a search engine. It supports DuckDuckGo (free, no API key), Serper, Brave, and TinyFish (all with free tiers). I also integrated Jina AI to fetch full articles for the LLMs to read. EVERYTHING in the tool is configurable, from system prompts to model temperatures. There are advanced debate models for the council. This tool is massive. Free and Fully Open Source. Check it out Repo: https://github.com/jacob-bd/the-ai-counsel submitted by /u/KobyStam [link] [comments]

PullMD v3: I let Claude design the MarkItDown integration, and it argued for keeping three of our own converters instead

About six weeks ago I posted PullMD here: a self-hosted Docker stack that turns any URL into clean Markdown, with an MCP server so Claude Code / Desktop / claude.ai pull pre-cleaned content instead of burning context on HTML boilerplate. v3.0.0 is out, and it's a bigger jump than the version number suggests. Short version: PullMD is no longer just a URL reader. It now converts documents, images, audio and YouTube videos to Markdown as well, and the default output got leaner. And no, don't worry - I'd like to think I haven't enshittified the original thing. Everything that worked before still works, (almost) unchanged. More on that "almost" below. How it started A boring personal itch. I had a pile of HTML files saved on disk that I wanted to hand to Claude, and figured PullMD already does the extraction, so why can't I just drop them in. So I added local file conversion: drag-and-drop on desktop, file picker on mobile, same Readability + Trafilatura pipeline. Local files are never cached, no share link. A few days later Microsoft released MarkItDown, and the next step was obvious: if I can take HTML files, why stop there. PDF, Word, PowerPoint, Excel, EPUB. So we wired MarkItDown in as a sidecar. Then we ripped three of its converters back out MarkItDown is good at the boring part: parsing document formats. For three other paths, Claude made the case for keeping our own instead - and once the reasons were sitting there in the code, pulling them was an easy call. Audio. MarkItDown's default audio path hands the file off to a cloud speech service. For a self-hosted tool we wanted that to be the operator's choice, not a default - so audio runs against any OpenAI-compatible endpoint you configure: a local faster-whisper / Ollama, a Groq Whisper, OpenAI, whatever. Nothing leaves your box unless you point it there. YouTube. MarkItDown's converter calls the transcript API outside its try/except, so a blocked or transcript-less video throws and takes the whole conversion down - you even lose the title and description that were already in the page HTML. No proxy support either, and YouTube rate-limits datacenter IPs. So we kept our own keyless handler: title + description + transcript, configurable timecodes and chunking, language preference, a proxy option, and a graceful fallback that still returns metadata when the transcript is gone. Image captioning. Rather than route captioning through MarkItDown's own LLM client, we put the vision call in our own provider layer: any OpenAI-compatible vision endpoint - a local Ollama / LLaVA, OpenAI, Gemini via a compatible gateway (defaults to gpt-4o-mini). Zero coupling, so a MarkItDown update can't break it - and if you only want media and no document conversion, you don't have to run the MarkItDown container at all. The principle we wrote into the project notes: use MarkItDown for file formats; keep the fragile, third-party-dependent paths in our own hands. What's actually new in v3 Documents → Markdown - PDF, DOCX, PPTX, XLSX, EPUB, ZIP, CSV, JSON, XML. By URL, by upload (POST /api/file), or drag-and-drop in the PWA. Needs the MarkItDown sidecar; leave it out and web pages work exactly as before. YouTube transcripts - title + description + full transcript, no API key. Images & audio → Markdown - opt-in, local-model-friendly, off by default (no model calls until you set a key). High-quality PDF tables (OCR) - PDFs convert free through the sidecar by default; for table-grade output there's an opt-in OCR tier (?pdf=ocr, reference provider Mistral OCR at ~$0.002/page, your own key, falls back to the free path on failure). Opt-in so it never silently costs money - and no, I didn't bundle a 4 GB local OCR engine with a 60-second cold start; it's a pluggable endpoint if you want one. Clean body by default - the one breaking change (the "almost" from up top). The body is now just # Title + content; source URL, fetch date and metadata moved into the YAML frontmatter, so nothing's duplicated and agents read fewer tokens. One-line opt-out: PULLMD_SOURCE_HEADER=true. Frontmatter field allowlist - trim the YAML to just the fields your pipeline reads. Everything past plain web extraction is opt-in and degrades gracefully. Configure nothing and v3 behaves like v2 with a cleaner body. Upgrade / self-host mkdir pullmd && cd pullmd curl -O https://raw.githubusercontent.com/AeternaLabsHQ/pullmd/main/docker-compose.yml docker compose up -d # → http://localhost:3000 Self-hosters on v2.x: clean-body is the only breaking change, MIGRATION.md has the opt-out. :latest now tracks v3; pin aeternalabshq/pullmd:2 to stay on the v2 output format. How it got built Same as v1: Claude Code wrote essentially all of the code, mostly with Opus 4.8. What I actually contributed was the planning and the pushback. The workflow was the superpowers plugin end to end: brainstorming to pin the design before a line of code, writing-plans to turn that into a structured plan, then sub

I need an alternative analysis path to Ollama. Possibly cloud analysis with agents like Claude

Hello everyone! I will try to be quick: i am developing a CLI application called Hosomaki. It currently requires Ollama to be installed and running locally. This creates a hard dependency on a full local model stack, which is a real barrier on servers or low-resource machines sadly 😞. If Ollama isn't running, hosomaki is entirely non-functional for now at least. I am working on a "Cloud analysis path with local sanitisation" path. The idea is to collects logs, scrub sensitive info and encrypt the payload asymmetrically, but i am a bit lost here honestly. Right now i do strict sanitising right now but I have no experience with the next steps. This is my repo: https://github.com/rivernova/hosomaki in case you want to understand the context. submitted by /u/SnooMachines9820 [link] [comments]

How to start using Claude the way it's suppose to, with agents and automation?

Hi all, Like the millions of others, my use of AI is super limited, to that simple chat window. However, the speed of how this tech is developing, I seem to be unable to figure out how to move to the next level on my own. When a company is exploring implementing AI, I'm guessing what they are actually exploring are the repetitive tasks and processes that can be reviewed and actioned by AI instead of a human, not necessarily to fully replace him or her, but to delegate the admin work to the AI. I like to be able to add that to my baggage of knwoledge and skills, creating AI-powered or supported processes/pipelines/flows. It might not be exactly what a specific company is looking for, but at least to convince them that I don't come in empty handed. I'm aware of the existence of the different Claude services, but probably the most important thing to know, I'm not a coder/programmer. I work on the business-side and usually collabo with someone at IT to get something made or improved. I also at one point had N8N installed on my home pc with Ollama and some local LLMs, yet nothing made. What are you recommendations to properly learn this, the AI companies are actually exploring? What are the most common (entry-level) functionalities companies ask for, is it a customer service chatbot? Theory is nice, but I really like to build things. Any help is welcome! Cheers. submitted by /u/SquidsAndMartians [link] [comments]

Switching from React Native + Node.js (4 YOE) to Agentic AI — need roadmap advice

I have 4 years of experience as a React Native and Node.js developer. I am comfortable with REST APIs, async/await, JSON, MongoDB, authentication, and shipping production apps. I am based in India. What I have learned so far: I recently completed an AI/LLM course that covered: • Pydantic (validation, models, serialization) • LLM theory (transformers, embeddings, attention, tokenization) • OpenAI and Gemini API integration • Prompt engineering (zero-shot, few-shot, CoT, persona prompting) • Prompt formats (ChatML, Alpaca, INST) • Ollama for local LLMs • FastAPI basics • Hugging Face model deployment • Agentic AI fundamentals — built a basic CLI coding agent What I understand conceptually: I understand that an AI agent = LLM brain + tools (Python functions) + agent loop + memory (messages list). I understand RAG, vector databases, the difference between fine-tuning and RAG, and how to structure a backend with Node.js calling a Python AI agent service when needed. What I want to do: I want to transition into Agentic AI / AI Engineer roles in India. I am not looking to become an ML researcher or train models. I want to build production AI agent systems — connecting LLMs to real business data, building tools, RAG pipelines, and shipping real products. My specific questions: 1. Is my current foundation strong enough to start building real agent projects or do I have gaps I am missing? 2. What should my learning roadmap look like for the next 3–6 months given my background? 3. Which frameworks should I prioritise — raw OpenAI API first, then LangChain/LangGraph, or jump straight to frameworks? 4. What kind of projects should I build for a strong portfolio targeting ₹20–35 LPA roles in India? 5. Any specific subreddits, communities, or resources beyond YouTube that helped you in this transition? My planned first 3 projects: • Simple agent with web search + calculator tool (no DB) • Agent connected to MongoDB with RAG • Full FastAPI backend wrapping the agent with a React frontend Any advice from people who have made a similar switch or are hiring in this space would be really helpful. Thanks. submitted by /u/rohitrai0101rm [link] [comments]

What started as a Claude Code scaffolding repo is now a full open-source AI harness (Maggy)

Last time I posted here it was about v5, the blast-score routing and a benchmark where it used 83% less Claude and still hit 100% success. A few people asked how it got to that point, so here's the longer version. Heads up first: I started this as a scaffolding repo, not a product. Every new project I'd end up re-teaching Claude Code the same stuff, coding standards, TDD, security gates, which CLIs to reach for. So I dumped it all into one place you drop into any repo with a single command. Run /initialize-project and the project just knows your conventions. That was the whole idea, make Claude Code consistent across projects. It kept growing from there. Every time I needed something day to day it ended up in the repo, and at some point it stopped being scaffolding and turned into an actual harness. It has a name now, Maggy. The short version of the arc: v3.6 cross-agent intelligence (Claude/Kimi/Codex/Ollama share skills + hooks) v4.0 Polyphony: container-isolated multi-agent orchestration (173 tests) v5.0 blast-score routing + self-correcting rules (596 tests) now one-config model routing, prompt pre-analysis, build-in-public agent What it does today: a local dashboard plus CLI that auto-bootstraps on startup. Every task gets a complexity score and goes to the cheapest model that can actually handle it, ollama and kimi for the easy stuff, codex in the middle, Claude for the hard or security-critical work. The routing rules live in YAML and correct themselves based on what actually worked. On top of that there's an intent graph that tracks why code exists and flags when the implementation drifts from it, a typed memory layer so goals survive context compaction, and a plugin system that auto-discovers anything you drop in. A few things landed since the v5 post that I'm happy with. You now pick your main model once and everything respects it, the hooks inside Claude Code, Maggy's own routing, and srooter (a gateway you can point Codex or anything Anthropic/OpenAI-compatible at). No setting it in five places, and cheap stuff still stays local. Every prompt also gets a quick pre-pass now. A fast model reads it and writes a short intent / scope / risks / approach note that gets handed to Claude before it starts, so it's working from a plan instead of cold. And the meta one: Maggy also has plugins support e.g one of the plugin is build-in-public which monitors updates to maggy or any project being built with maggy and posts updates on LinkedIn, X and Reddit. Worth being straight about the tradeoffs. It's one person's harness that grew organically, so it's broad and some corners are rough. The v5 benchmark caught real gaps, local models are bad at prose and nothing was writing tests, both fixed with force-routes now. Quality lands a hair under pure Claude, 7.4 vs 7.8 in that benchmark, for 83% less premium spend. Not a free lunch, just a tradeoff I'll take most days. Moving my focus fully onto Maggy from here. Repo: https://www.github.com/alinaqi/maggy . Clone it, run ./install.sh, then /initialize-project in any Claude Code session. /maggy-init if you want the dashboard and routing. Happy to get into any of it. https://preview.redd.it/6oj4m3j4wx5h1.png?width=3024&format=png&auto=webp&s=4896a4227a2d02a1b410bb5d4a35923080a2a003 submitted by /u/naxmax2019 [link] [comments]

Has anyone actually replaced Claude Code / Codex with local models on an Macbook Pro M5 Max 128GB?

Considering buying a maxed out MacBook Pro M5 Max with 128GB of RAM and one of the things I want to figure out before pulling the trigger is whether local models are good enough to actually replace cloud AI coding tools. My current setup is Claude Code on a Max subscription plus GitHub Copilot through work. It works well but I'm curious if local models have gotten good enough to actually replace that, not just supplement it. Not talking about occasional use or running smaller models for autocomplete. I mean fully replacing the agentic stuff, the multi-file edits, the back and forth reasoning that Claude Code handles. Can local models actually keep up with that workload on this hardware? If you made the switch, what are you running? Ollama, LM Studio, something else? Which models? And honestly, what did you have to give up, if anything? submitted by /u/Brazeuslian [link] [comments]

Claude Code 2.1.165 + Ollama (qwen3:8b / qwen2.5-coder:7b) instantly throws "response exceeded 32000 output token maximum" even for "hi"

I'm trying to use Claude Code with local Ollama models, but every prompt fails with: The strange part is that it happens even for extremely small prompts like: hi say apple What is 1+1? Answer with only one character. My setup Claude Code: 2.1.165 (Windows) Ollama: 0.30.5 Models tested: qwen3:8b qwen2.5-coder:7b Launch method: $env:ANTHROPIC_BASE_URL="http://localhost:11434" $env:ANTHROPIC_AUTH_TOKEN="ollama" $env:ANTHROPIC_API_KEY="" claude --model qwen3:8b Things I've already tested ollama run qwen3:8b works perfectly ollama run qwen2.5-coder:7b works perfectly Disabled Thinking Mode in Claude Code Changed CLAUDE_CODE_MAX_OUTPUT_TOKENS Started completely fresh sessions Used /clear Deleted/renamed my entire .claude directory and let Claude recreate it Tested multiple models Verified Ollama API endpoints These all work: Invoke-RestMethod http://localhost:11434/api/version Invoke-RestMethod http://localhost:11434/api/tags Invoke-RestMethod http://localhost:11434/v1/models Additional observation /doctor never mentions Ollama or a custom provider. It still shows: ✓ First-party provider (api.anthropic.com) which makes me wonder if Claude Code 2.1.165 no longer properly supports the old ANTHROPIC_BASE_URL=http://localhost:11434 workaround. Has anyone recently gotten Claude Code 2.1.165 working directly with Ollama? If so, what exact configuration are you using? https://preview.redd.it/21lu04yzvj5h1.png?width=1690&format=png&auto=webp&s=5cc6bcab83a7362eb3667e27d3267ca873136d64 submitted by /u/Kindly-Kitchen4408 [link] [comments]

We built a source-available LLM reliability library (free for research / personal / internal eval) that can cut inference cost by half at matched quality, and you adopt it by changing one import [P] [R]

TL;DR: Reliability techniques (methods that boost an LLM's correctness by spending extra inference, e.g., retries with feedback, ensembling, generator/critic refinement, verification passes, difficulty-aware routing) are scattered across the literature, each in its own paper-specific codebase. We unified 28 reliability techniques (21 communication-theoretic methods across 6 families plus 7 prior-method baselines: Self-Consistency, Self-Refine, CoVe, BoN, Weighted BoN, CISC, MoA), each measured against an uncoded single-pass baseline, under a single API, with 3 adaptive routers (SemKNN + two local ACM routers) sitting on top, then showed that routing the technique adaptively per prompt lets you slide along a quality/cost frontier. In our paper benchmark with one specific lineup, Nemotron + Devstral as the two generators and GLM-5.1 as the judge, the adaptive router delivered ~56% cost reduction at matched quality, or ~7% quality bump at matched cost, vs the best fixed method we compared against at that same lineup. One knob (λ) does the sliding. The qualitative pattern (adaptive beats fixed) should generalize, but absolute numbers are lineup-specific, and we haven't run the full sweep across other model combinations yet. Adoption is change one import: python - from openai import OpenAI + from agentcodec.openai import OpenAI Pass reliability="harq_ir" (or any of the 28 techniques) and existing client.chat.completions.create(...) calls keep their native OpenAI response shape. Same drop-in shims for Anthropic and Ollama. GitHub: https://github.com/intellerce/agentcodec Working paper: https://arxiv.org/abs/2605.09121 After spending a while researching reliability methods from papers, we kept hitting the same wall: every paper ships its own one-off codebase with its own prompt format, its own scoring rubric, its own model wrapper. Benchmarking "should we use self-refine or best-of-N here?" turned into a week of plumbing per comparison. The communication-theory framing is what tied it together: an LLM is a stochastic channel Y = A(X) + N, and every reliability technique from the wireless world has a direct analog in agent-land: Wireless Agent-land ARQ / HARQ retry-with-feedback loops Diversity combining (MRC/SC/EGC) ensemble multiple models Turbo decoding iterative generator/critic mutual refinement Fountain codes rateless sampling, stop when the judge is confident FEC answer + structured parity passes (re-derivation, verification, alternative), decode by cross-check ACM (adaptive coding-modulation) route by difficulty We put all of them in one library: 28 reliability techniques (the 7 prior-method baselines are part of that 28, not on top of it), plus the uncoded single-pass baseline they're all measured against, plus 3 adaptive routers (SemKNN + two local ACM routers) that select a technique per prompt. Full breakdown in the README. The minimal version ```python from agentcodec import ReliabilityModule mod = ReliabilityModule.from_dict({ "models": [ # Spatial diversity: two different families = uncorrelated errors {"model": "qwen3:8b", "base_url": "http://localhost:11434/v1", "api_key": "ollama"}, {"model": "llama3.1:8b", "base_url": "http://localhost:11434/v1", "api_key": "ollama"}, ], "judge": {"model": "gemma3:12b", "base_url": "http://localhost:11434/v1", "api_key": "ollama"}, "critic": {"same": True}, "strategy": {"type": "fixed", "technique": "harq_ir", "params": {"max_rounds": 4}}, }) result = mod.run("Prove the sum of the first n odd integers is n2.", category="reasoning") print(result.text, result.cost_usd, result.cost_source, result.technique_used) ``` Swap "harq_ir" for "diversity_mrc", "turbo", "fountain", etc. Same API, same ReliabilityResult shape, same cost-source tier on every output. For production, flip strategy to routed and the library picks the technique per prompt (cheap baseline on easy prompts, diversity_mrc on hard ones). Three things worth calling out Beyond the technique catalog, three pieces of the implementation that took real work: 1. Native async streaming for all but 2 techniques (acm_soft, acm_learned), with role-tagged events. mod.astream() drives AsyncOpenAI / AsyncAnthropic / httpx.AsyncClient end-to-end (no worker-thread bridge) and emits TokenEvents tagged with a role: "answer", "thinking", "draft", "critique", "verification", "candidate", "synthesis". So when you stream a HARQ-IR run, you can render the round-by-round drafts and critiques live, not just the final answer: python async for ev in mod.astream("Explain QUIC vs TCP."): if isinstance(ev, TokenEvent): if ev.role == "answer": print(ev.text, end="", flush=True) elif ev.role == "draft": print(f"\n[draft] {ev.text}") elif ev.role == "critique": print(f"\n[CRITIC] {ev.text}") elif ev.role == "thinking": pass # captured to result.thinking_text elif isinstance(ev, FinalEvent): print(f"\ndone — {ev.result.technique_used}, " f"thinking_cost=${ev.result.thinking_cost_usd:.4f}

Google’s Gemma 4 12B just dropped - here’s how to run it locally on your Mac

Google released Gemma 4 12B today. It’s a solid open-source model (Apache 2.0) that’s multimodal and runs really well on Macs with 16GB or more unified memory. Good at reasoning, coding, and agent stuff. Quick Mac-friendly info • 12B parameters, fits nicely on M2/M3/M4 Macs (especially with Q4/Q5 quant) • 256K context • Text + vision + audio support Easiest way to run it: Ollama 1. Download and install Ollama from ollama.com (the Mac app is super simple). Or use Homebrew if you prefer. 2. Open Terminal and pull the model: ollama pull gemma4:12b 3. Run it: ollama run gemma4:12b That’s it. You can start chatting right away. Mac tips: • Ollama uses Metal automatically so it runs pretty fast on Apple Silicon. • 16GB Macs handle the 12B model fine. 32GB feels even better. • Great for pairing with Continue.dev in VS Code if you code a lot. Other options if Ollama isn’t your thing: LM Studio (nice GUI), or llama.cpp for more control. Has anyone tried the image or audio features locally yet? How fast is it on your machine? Drop your specs and results if you test it.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ submitted by /u/nullvector88 [link] [comments]

I made a calendar/dashboard on a raspberry pi to help my wife and myself manage our schedules. It displays on a 77 inch OLED. I made a companion app for her phone that uses Apple Intelligence and Qwen installed on the Pi to clean up entries. She travels ~%50 of the year.

All the calendar entries and stuff about work are fabricated in the screenshots. This was kind of annoying to do but I wanted to share anyway so I put the effort in. It's actually prettier on the OLED than the screenshots do it justice. (e.g., the flowers on the right in dark mode look almost like they're suspended in ether that's a little lost here.) I did this last time: https://www.reddit.com/r/ClaudeAI/comments/1tbjp08/sonos_quit_supporting_their_mac_app_and_my_wife/ I am writing this top portion without Claude. As a quick reminder I am an IP lawyer. I am not a coder/developer. But I'm having fun making things with Claude/Claude Code for myself and my wife to use. (And also some work stuff that's not very fun but does a lot for me as a an IP/Trademark attorney.) Top line summary: built a calendar/dashboard on a Raspberry Pi for my 77-inch OLED to help organize kids/wife's travel schedule/my schedule, and built a companion iOS app mostly trying to make something pretty so my wife will actually want to use it. I'm not selling anything. I am posting a hobby project mostly just to show what I did and get feedback. The user base is 2. It might expand to include my kids. My wife has terrible eyesight so part of this is driven by her eyes. At home the "Almanac" displays on a 77-inch OLED in our bedroom, which has a lofted office. My wife is a neuroscientist. She travels ~50%. "Hey, while you're awake [5am], could you tell me what the weather is in [city 1] and [city 2]?" "...what are the dates you're going to be there?" "[City 1] today, [City 2] I'm not sure. Could you open the calendar for me while I pack?" I also routinely shout calendar entries at Siri, and Siri is not good at understanding my deep voice, so I have another AI, Qwen, on the Pi that audits entries. (E.g., Coffee with chris Evan's becomes Coffee with Chris Evans.) My wife wanted to take pictures of text and turn them into calendar entries. The phone extracts the event with Apple Intelligence and writes it straight to the calendar. The Pi's Qwen pass — the same one that audits my Siri entries — then catches OCR typos and miscapitalized names. I might at some point use Haiku but the idea of something that runs locally on a Pi without tokens was appealing (and for the use case I think Haiku might be using a small atomic weapon to kill a mosquito). If you tap the weather in the last panel of Bouquet it will give you granular weather if you're close enough in time for it to populate for that day and give photography recommendations. (e.g., golden hour) The photography stuff was just kind of me being gratuitous though. I was never grumpy about the early-morning wake-up and help-with-logistics chats. (I'm still more than happy to wake up before dawn, have a coffee, and talk things through.) But I figured out that the same questions came up a lot and went to work trying to put the answers in one place. My wife is basically blind though. There are subscription services that kind of do what I want, and devices you can buy that are basically just cheap iPads that can't do very much. I wasn't interested in either, and I know my wife wouldn't use them because they're not pretty to look at. My first thought was that if my smart TV could support a bunch of disorganized garbage apps I'll never open, then clearly they would love to host my indie app for two people in some fashion. That was stupid on my part. Smart TVs don't anticipate people coding for just their home and mostly want to broker deals between Netflix and Prime for who gets top billing on their OS which ends up looking like something an ADHD squirrel with a subscription addiction would make. So I bought a Raspberry Pi and went to work making a kiosk with our shared calendar that also pulls in other calendars we both use. It all pipes into the Pi and turns on automatically in the morning on our 77-inch OLED. The companion app uses a lot of the same things the dash does, but also ties in Apple Intelligence to streamline calendar entries from scanning photos. Building this, I knew my wife would never use it unless it was pretty, and this is one of those cases where the form is the function. I made sure it's something she wanted to use. Unexpected tool that proved useful: the Pi's dashboard server only listens on localhost — nothing's port-forwarded or exposed to the internet — and Tailscale republishes it on my private tailnet over HTTPS, so the phone app just points at one stable hostname and reaches it from anywhere. That means the wall and the phone read the same backend whether I'm home or not, and the only devices that can even see it are the ones signed into my tailnet. Here's Claude's take on it: How it works: the shared calendar lives in iCloud, and the Raspberry Pi is the brain sitting in front of it. The Pi pulls events from iCloud over CalDAV, folds in weather, and does two jobs at once — it renders the wall "Almanac" in a Chromium kiosk, and it publishes a clean