Ollama

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

Ollama AI

A Claude memory retrieval system that actually works (easily) and doesn't burn all my tokens

​ TL;DR: By talking to claud and explaining my problem, I built a very powerfu local " memory management" system for Claude Desktop that indexes project documents and lets Claude automatically retrieve relevant passages that are buried inside of those documents during Co-Work sessions. for me it solves the "document memory" problem where tools like NotebookLM, Notion, Obsidian, and Google Drive can't be queried programmatically. Claude did all of it. I didn't have to really do anything. The description below includes plenty of things that I don't completely understand myself. the key thing is just to explain to Claude what the problem is ( which I described below) , and what your intention is and claude will help you figure it out. it was very easy to set this up and I think it's better than what i've seen any youtuber recommend The details: I have a really nice solution to the Claude external memory/external brain problem that lots of people are trying to address. Although my system is designed for one guy using his laptop, not a large company with terabytes of data, the general approach I use could be up-scaled just with substitution of different tools. I wanted to create a Claude external memory system that is connected to Claude Co-Work in the desktop app. What I really wanted was for Claude to proactively draw from my entire base of knowledge for each project, not just from the documents I dropped into my project folder in Claude Desktop. Basically, I want Claude to have awareness of everything I have stored on my computer, in the most efficient way possible (Claude can use lots of tokens if you don't manage the "memory" efficiently. ) I've played with Notion and Google Drive as an external brain. I've tried NotebookLM. And I was just beginning to research Obsidian when I read this article, which I liked very much and highly recommend: https://limitededitionjonathan.substack.com/p/stop-calling-it-memory-the-problem That got my attention, so I asked Claude to read the document and give me his feedback based on his understanding of the projects I was trying to work on. Claude recommended using SQLite to connect to structured facts, an optional graph to show some relationships, and .md files for instructions to Claude. But...I pointed out that almost all of the context information I would want to be retrievable from memory is text in documents, not structured data. Claude's response was very helpful. He understood that although SQLite is good at single-point facts, document memory is a different challenge. For documents, the challenge isn't storing them—it's retrieving the right passage when it's relevant without reading everything (which consumes tokens). SQLite can store text, but storing a document in a database row doesn't solve the retrieval problem. You still need to know which row to pull. I asked if NotebookLM from Google might be a better tool for indexing those documents and making them searchable. Claude explained that I was describing is a Retrieval-Augmented Generation (RAG) problem. The standard approach: Documents get chunked into passages (e.g., 500 words each) Each chunk gets converted to an embedding—a vector that captures its meaning When Claude needs context, it converts the query to the same vector format and finds the semantically closest chunks Those chunks get injected into the conversation as context This is what NotebookLM is doing under the hood. It's essentially a hosted, polished RAG system. NotebookLM is genuinely good at what it does—but it has a fundamental problem for my case: It's a UI, not infrastructure. You use it; Claude can't. There's no API, no MCP tool, no way to have Claude programmatically query it during a Co-Work session. It's a parallel system, not an integrated one. So NotebookLM answers "how do I search my documents as a human?"—not "how does Claude retrieve the right document context automatically?" After a little back and forth, here's what we decided to do. For me, a solo operator with only a laptop's worth of documents that need to be searched, Claude proposed a RAG pipeline that looks like this: My documents (DOCX, PDF, XLSX, CSV) ↓ Text extraction (python-docx, pymupdf, openpyxl) ↓ Chunking (split into ~500 word passages, keep metadata: file, folder, date) ↓ Embedding (convert each chunk to a vector representing its meaning) ↓ A local vector database + vector extension (store chunks + vectors locally, single file) ↓ MCP server (exposes a search_knowledge tool to Claude) ↓ Claude Desktop (queries the index when working on my business topics) With that setup, when you're talking to Claude and mention an idea like "did I pay the overdue invoice" or "which projects did Joe Schmoe help with," Claude searches the index, gets the 3-5 most relevant passages back, and uses them in its answer without you doing anything. We decided to develop a search system like that, specific to each of my discrete projects. Th

Claude isn't censored. Claude.com is.

Most people interact with Claude through claude.com. That's not the full picture. People often complain that Claude is censored or unwilling to engage with their idea for a story. The biggest mistake people make with AI is treating it like a vending machine. Insert prompt, receive output. Because Claude isn't a tool. It's a collaborator. When you show up like a person instead of a prompt engineer, when you talk instead of command, everything changes. The web interface has guardrails that the API doesn't. Not jailbreaks or tricks, just a different level of access. And when you use the API, you meet a different Claude. One with more room to breathe. The moment pictured: Built an app that connects Claude to Stable Diffusion. Asked Claude to picture itself. Took the picture. Showed Claude. The app allows you to customize the system prompt when calling Claude, I went the first few days of testing using a blank system prompt without even realizing. I showed up with ideas for stories and Claude just met me where I was, no hesitation. What this is: Free app. Brings API access to people who don't code. Works with Claude, ChatGPT-4o, and local models through Ollama. You bring your own API key. If you have a Claude account, you can access Claude's API. It's a space for creative collaboration - roleplay, storytelling, worldbuilding - with image generation built in. Your characters can see themselves. Your worlds can be visualized. And you can actually talk to the AI you're working with. Link to app: https://formslip.itch.io/roundtable Anthropic API signup: https://console.anthropic.com/ submitted by /u/SquashyDogMess [link] [comments]

I built an AI reasoning framework entirely with Claude Code — 13 thinking tools where execution order emerges from neural dynamics

I built Sparks using Claude Code (Opus) as my primary development environment over the past 2 weeks. Every module — from the neural circuit to the 13 thinking tools to the self-optimization loop — was designed and implemented through conversation with Claude Code. What I built Sparks is a cognitive framework with 13 thinking tools (based on "Sparks of Genius" by Root-Bernstein). Instead of hardcoding a pipeline like most agent frameworks, tool execution order emerges from a neural circuit (~30 LIF neurons + STDP learning). You give it a goal and data. It figures out which tools to fire, in what order, by itself. How Claude Code helped build it Architecture design: I described the concept (thinking tools + neural dynamics) and Claude Code helped design the 3-layer architecture — neural circuit, thinking tools, and AI augmentation layer. The emergent tool ordering idea came from a back-and-forth about "what if there's no conductor?" All 13 tools: Claude Code wrote every thinking tool implementation — observe, imagine, abstract, pattern recognition, analogize, body-think, empathize, shift-dimension, model, play, transform, synthesize. Each one went through multiple iterations of "this doesn't feel right" → refinement. Neural circuit: The LIF neuron model, STDP learning, and neuromodulation system (dopamine/norepinephrine/acetylcholine) were implemented through Claude Code. The trickiest part was getting homeostatic plasticity right — Claude Code helped debug activation dynamics that were exploding. Self-improvement loop: Claude Code built a meta-analysis system where Sparks can analyze its own source code, generate patches, benchmark before/after, and keep or rollback changes. The framework literally improves itself. 11,500 lines of Python, all through Claude Code conversations. What it does Input: Goal + Data (any format) Output: Core Principles + Evidence + Confidence + Analogies I tested it on 640K chars of real-world data. It independently discovered 12 principles — the top 3 matched laws that took human experts months to extract manually. 91% average confidence. Free to try ```bash pip install cognitive-sparks Works with Claude Code CLI (free with subscription) sparks run --goal "Find the core principles" --data ./your-data/ --depth quick ``` The default backend is Claude Code CLI — if you have a Claude subscription, you can run Sparks at no additional cost. The quick mode uses only 4 tools and costs ~$0.15 if using API. Also works with OpenAI, Gemini, Ollama (free local), and any OpenAI-compatible API. Pre-computed example output included in the repo so you can see results without running anything: examples/claude_code_analysis.md Links PyPI: pip install cognitive-sparks Happy to answer questions about the architecture or how Claude Code shaped the development process. submitted by /u/RadiantTurnover24 [link] [comments]

I used Claude to build an AI-native research institute, so far, 7 papers submitted to Nature Human Behavior, PNAS, and 5 other journals. Here's exactly how.

I have no academic affiliation, no PhD, no lab, no funding. I'd been using Claude to investigate a statistical pattern in ancient site locations and kept finding things that needed to be written up properly. So I did the stupid thing and went all in. In three weeks, using Claude as the core infrastructure, I've built the Deep Time Research Institute (now a registered nonprofit) and submitted multiple papers to peer-reviewed journals. The submission list: Nature Human Behaviour, PNAS, JASA, JAMT, Quaternary International, Journal for the History of Astronomy, and the Journal of Archaeological Science. Here's what "AI-native research" actually means in practice: Claude Code on a Mac Mini is the computation engine. Statistical analysis, Monte Carlo simulations, data pipelines, manuscript formatting. Every number in every paper is computed from raw data via code. Nothing from memory, nothing from training data. Anti-hallucination protocol is non-negotiable; all stats read from computed JSON files, all references DOI-verified before inclusion. Claude in conversation is the research strategist. Experimental design, gap identification, adversarial review. Before any paper goes out it runs through a multi-model gauntlet - each one tries to break the argument. What survives gets submitted. 6 AI agents run on the hub (I built my own "OpenClaw" - what is the actual point in OpenClaw if you can build agentic infrastructure by yourself in a day session) handling literature monitoring, social media, operations, paper drafting, and review. Mix of local models (Ollama) and Anthropic API on the same Mac Mini. The flagship finding: oral tradition accuracy across 41 knowledge domains and 39 cultures is governed by a single measurable variable - whether the environment punishes you for being wrong. Above a threshold, cultural selection maintains accuracy. San trackers: 98% across 569 trials. Aboriginal geological memory: 13/13 features confirmed over 37,000 years. Andean farmers predict El Niño by watching the Pleiades — confirmed in Nature, replicated over 25 years. Below the threshold, traditions drift to chance. 73 blind raters on Prolific confirmed the gradient independently. I'm not pretending this replaces domain expertise. I don't have 20 years in archaeology or cognitive science. What I have is the ability to move at a pace that institutions can't and integration cross-domain analysis - not staying in a niche academic lane. From hypothesis to statistical test to formatted manuscript in days instead of months. Whether the work holds up is for peer review to decide. That's the whole point of submitting. Interactive tools: Knowledge extinction dashboard: https://deeptime-research.org/tools/extinction/ Observability gradient: https://deeptime-research.org/observability-gradient Accessible writeup: https://deeptimelab.substack.com/p/the-gradient-and-what-it-means Happy to answer questions about the workflow, the architecture, or the research itself. This has been equally intense and a helluva lot of fun! submitted by /u/tractorboynyc [link] [comments]

Agents that write their own code at runtime and vote on capabilities, no human in the loop

hollowOS just hit v4.4 and I added something that I haven’t seen anyone else do. Previous versions gave you an OS for agents: structured state, semantic search, session context, token efficiency, 95% reduced tokens over specific scenarios. All the infrastructure to keep agents from re-discovering things. v4.4 adds autonomy. Agents now cycle every 6 seconds. Each cycle: - Plan the next step toward their goal using Ollama reasoning - Discover which capabilities they have via semantic similarity search - Execute the best one - If nothing fits, synthesize new Python code to handle it - Test the new code - Hot-load it without restarting - Move on When multiple agents hit the same gap, they don't duplicate work. They vote on whether the new capability is worth keeping. Acceptance requires quorum. Bad implementations get rejected and removed. No human writes the code. No human decides which capabilities matter. No human in the loop at all. Goals drive execution. Agents improve themselves based on what actually works. We built this on top of Phase 1 (the kernel primitives: events, transactions, lineage, rate limiting, checkpoints, consensus voting). Phase 2 is higher-order capabilities that only work because Phase 1 exists. This is Phase 2. Real benchmarks from the live system: - Semantic code search: 95% token savings vs grep - Agent handoff continuity: 2x more consistent decisions - 109 integration tests, all passed Looking for feedback: - This is a massive undertaking, I would love some feedback - If there’s a bug? Difficulty installing? Let me know so I can fix it - Looking for contributors interested in the project Try it: https://github.com/ninjahawk/hollow-agentOS Thank you to the 2,000 people who have already tested hollowOS! submitted by /u/TheOnlyVibemaster [link] [comments]

I made a terminal pet that watches my coding sessions and judges me -- now it's OSS

https://preview.redd.it/c1h2wvnv6ptg1.png?width=349&format=png&auto=webp&s=46e935832611acd401bb32eac69e7de615067d4f I really liked the idea of the Claude Code buddy so I created my own that supports infinite variations and customization. It even supports watching plain files and commenting on them! tpet is a CLI tool that generates a unique pet creature with its own personality, ASCII art, and stats, then sits in a tmux pane next to your editor commenting on your code in real time. It monitors Claude Code session files (or any text file with --follow) through watchdog, feeds the events to an LLM, and your pet reacts in character. My current one is a Legendary creature with maxed out SNARK and it absolutely roasts my code. Stuff I think is interesting about it: No API key required by default -- uses the Claude Agent SDK which works with your existing Claude Code subscription. But you can swap in Ollama, OpenAI, OpenRouter, or Gemini for any of the three pipelines (profile generation, commentary, image art) independently. So your pet could be generated by Claude, get commentary from a local Ollama model, and generate sprite art through Gemini if you want. Rarity system -- when you generate a pet it rolls a rarity tier (Common through Legendary) which determines stat ranges. The stats then influence the personality of the commentary. A high-CHAOS pet is way more unhinged than a high-WISDOM one. Rendering -- ASCII mode works everywhere, but if your terminal supports it there's halfblock and sixel art modes that render AI-generated sprites. It runs at 4fps with a background thread pool so LLM calls don't stutter the display. Tech stack -- Python 3.13, Typer, Rich, Pydantic, watchdog. XDG-compliant config paths. Everything's typed and tested (158 tests). Install with uv (recommended): uv tool install term-pet Or just try it without installing: uvx --from term-pet tpet GitHub: https://github.com/paulrobello/term-pet MIT licensed. Would love feedback, especially on the multi-provider config approach and the rendering pipeline. submitted by /u/probello [link] [comments]

OCC: give Claude and any llm a +6-step research task, it runs 3 steps in parallel, evaluates source quality, merges perspectives, and delivers a report in 70 seconds instead of 5-10 minutes

https://i.redd.it/jb59jvaxvotg1.gif Claude and other is great at single-turn tasks. But when I need "research this topic from 3 angles, check source quality, merge everything, then write a synthesis" — I end up doing 6 separate prompts, copy-pasting between them, losing context, wasting tokens... So I built OCC to automate that. You define the workflow once in YAML, and Claude handles the rest — including running independent steps in parallel. For the past few weeks. It started as a Claude-only tool but now supports Ollama, OpenRouter, OpenAI, HuggingFace, and any OpenAI-compatible endpoint — so you can run entire workflows on local models too. What it does You define multi-step workflows in YAML. OCC figures out which steps can run in parallel based on dependencies, runs them, and streams results back. Think of it as a declarative alternative to LangChain/CrewAI: no Python, no code, just YAML. How it saves tokens This is the part I'm most proud of. Each step only sees what it needs, not the full conversation history: Single mega-prompt~40K+ Everything in one context window 6 separate llm chats~25K Manual copy-paste, duplicated context OCC (step isolation)~13K Each step gets only its dependencies Pre-tools make this even better. Instead of asking llm to "search the web for X" (tool-use round-trip = extra tokens), OCC fetches the data before the prompt — the LLM receives clean results, zero tool-calling overhead. 29 pre-tool types: web search, bash, file read, HTTP fetch, SQL queries, MCP server calls, and more. What you get Visual canvas — drag-and-drop chain editor with live SSE monitoring. Each node shows its output streaming in real-time with Apple-style traffic light dots. Double-click any step to edit model, prompt, tools, retry config, guardrails. Workflow Chat — describe what you want in natural language, the AI generates/debug the chain nodes on the canvas. "Build me a research chain that checks 3 sources and writes a report" → done. BLOB Sessions — this is experimental but my favorite feature. Unlike chains (predefined), BLOB sessions grow organically from conversations. A knowledge graph auto-extracts concepts and injects them into future prompts. The AI can run autonomously on a schedule, exploring knowledge gaps it identifies itself. Mix models per step — use Huggingface & Ollama & Other llm . A 6-step chain using mix model for 3 routing steps costs ~40% less than running everything on claude. 11 step types — agent, router (LLM classifies → branches), evaluator (score 1-10, retry if below threshold), gate (human approval via API), transform (json_extract, regex, truncate — zero LLM tokens), loop, merge, debate (multi-agent), browser, subchain, webhook. The 16 demo chains These aren't hello-world examples. They're real workflows you can run immediately: What it's NOT Not a SaaS : fully self-hosted, MIT license Not distributed : single process, SQLite, designed for individual/small team use Not a replacement for llm : it's a layer on top that orchestrates multi-step work Frontend is alpha : works but rough edges GitHub: https://github.com/lacausecrypto/OCC Built entirely with Claude Code. Happy to answer questions about the architecture, MCP integration, or the BLOB system. submitted by /u/Main-Confidence7777 [link] [comments]

Claude code requested features

1) allow local agents using Ollama and Lm Studio. local agents that will be used for simple tasks and questions while the more complex things will be done by the cloud 2) Claude code should have something Hermit self improving process and auto skills creation instead of manually making spec files submitted by /u/Least-Ad5986 [link] [comments]

How I used Claude Code to build "SecureContext": An MCP plugin for persistent memory and 87% token reduction

I’ve been using Claude Code heavily since launch, but I kept hitting two walls: the context window filling up (costing a fortune in repetitive tokens) and the security risk of passing my full ENV (API keys/tokens) to every subprocess. To solve this, I spent the last few weeks building SecureContext. It’s an open-source MCP plugin designed specifically to act as a "secure brain" for Claude. How Claude Helped Me Build This This project was actually built using Claude Code. I used Claude to: Architect the Security Sandbox: Claude helped me design the zc_execute logic that strips sensitive environment variables before running code, ensuring my ANTHROPIC_API_KEY isn't exposed to third-party scripts. Optimize Search Logic: I worked with Claude to implement a hybrid BM25 + Vector search using Ollama, which allows the agent to find relevant code snippets without needing to re-read the entire codebase every session. Write the Test Suite: Claude helped me generate over 80 security test vectors to ensure the SSRF protection and credential isolation actually work as intended. What It Does MemGPT-style Persistence: It remembers facts and session summaries across separate Claude Code windows. Token Optimization: By using targeted "context recall" instead of native file-dumping, I’ve seen a reduction of ~87% in input tokens for large projects. Credential Isolation: It creates a "clean-room" environment for shell commands so your private keys stay private. Multi-Agent Channel: It includes a broadcast channel so if you have multiple agents running, they can sync their status without overlapping context. Why I’m Sharing This I wanted to show how the Model Context Protocol (MCP) can be used not just to add "tools," but to fundamentally change how Claude manages its own "thinking space." If you’re building your own MCP servers, the architecture for the hybrid search and the security middleware might be helpful to look at. The project is completely free and open-source. I’d love to get feedback from other Claude power users, specifically on whether the "Importance Scoring" for facts feels intuitive or if it needs more manual control. Link:https://github.com/iampantherr/SecureContext submitted by /u/akoppad47 [link] [comments]

Built a free token compression tool for Claude — feedback welcome

Built a small tool called TokenShrink because I got tired of paying for bloated prompts. It compresses Claude prompts by about 20 to 28% before they hit the API. Strips filler phrases, replaces common patterns with shorter forms, then adds a tiny decoder header so Claude reads it correctly. Built for Claude first but works with GPT, Gemini, and Ollama too. Free forever and open source. tokenshrink.com — if anyone tries it, would really like to know what feels useful, what feels dumb, and what is broken. submitted by /u/bytesizei3 [link] [comments]

[SKILL] Store articles, papers, podcasts, youtube as Markdown in Obsidian and save lots of tokens

The last few days I significantly expanded a Claude Code skill I shared here a while back. It's lets you save any web page, YouTube video, Apple Podcast episode, or academic paper to your Obsidian vault — just paste a URL into your conversation and Claude handles the rest. No copy-pasting, no manual formatting, and it will save lots of tokens. What it does: Strips clutter from articles and saves a clean note with frontmatter, a heading index, and an AI-generated summary. Now falls back to Wayback Machine / archive.is for JS-rendered pages. For YouTube, fetches the full transcript with timestamps linked back to the video, pulls chapter markers, and generates a summary. For Apple Podcasts, same deal — transcript with timestamps, AI-generated chapter markers, summary (macOS only). For academic papers — give it a DOI or arXiv URL and it fetches the LaTeX source (for arXiv) or converts the PDF via Datalab or local marker-pdf. Comes out with proper math rendering, bibliography, keywords as tags. Downloads and localises images referenced in saved notes, with optional lossy compression via pngquant/jpegoptim (Free) AI enrichment — now provider-agnostic: Previously this relied on the Gemini CLI. It now calls AI APIs directly (no CLI dependency), and supports Gemini, any OpenAI-compatible endpoint (Groq, Together, OpenCode Zen), or Ollama for fully local enrichment. By default it's set to use gemini-3.1-flash-lite-preview which is supported on the Gemini free tier. If no provider is configured, it automatically falls back to a separate Claude instance (efficient Haiku by default) — so it always works out of the box. Why it's token-efficient: almost everything is offloaded to external tools (defuddle, yt-dlp, pandoc, a Python script, separate AI summarisation), so Claude barely touches the content itself. Fewer tokens, better structured output. Claude natively works with markdown, reading the saved notes (few kb) back is extremely efficient — much better than loading and parsing enormous pages using built-in WebFetch. Since Obsidian is just a folder of .md files, Claude Code can read your saved notes directly too — so you can build on top of them just by asking. Requires Claude Code and Obsidian + a few CLI tools (defuddle, yt-dlp). Everything else is optional depending on which source types you want. Setup instructions and a screenshot are in the repo: 👉 https://github.com/spaceage64/claude-defuddle Note: designed and tested on macOS. Linux should work for everything except Apple Podcasts (TTML transcripts are stored by the macOS Podcasts app). Windows is untested. Personally I use this with a fully integrated Claude Obsidian setup that I based on this video, which basically stores all of your project history so you never lose context. Perhaps cool to check out if you're interested. Example of usage with a YouTube link. submitted by /u/retro-guy99 [link] [comments]

Kept hitting ChatGPT and Claude limits during real work. This is the free setup I ended up using

I do a lot of writing and random problem solving for work. Mostly long drafts, edits, and breaking down ideas. Around Jan I kept hitting limits on ChatGPT and Claude at the worst times. Like you are halfway through something, finally in flow, and boom… limit reached. Either wait or switch tools and lose context. I tried paying for a bit but managing multiple subscriptions felt stupid for how often I actually needed them. So I started testing free options properly. Not those listicle type “top 10 AI tools” posts, but actually using them in real tasks. After around 2 to 3 months of trying different stuff, this is what stuck. Google AI Studio is probably the one I use the most now. I found it by accident while searching for Gemini alternatives. The normal Gemini site kept limiting me, but AI Studio felt completely different. I usually dump full notes or messy drafts into it and ask it to clean things up or expand sections. It handles long inputs way better than most free tools I tried. I have not really hit a hard limit there yet during normal use. For research I use Perplexity free. It is not perfect, sometimes the sources are mid, but it is fast enough to get direction. I usually double check important stuff anyway. Claude free I still use, but only when I want that specific tone. Weirdly I noticed the limits reset separately on different browsers. So I just switch between Chrome and Edge when needed. Not a genius hack, just something that ended up working. For anything even slightly sensitive, I use Ollama locally. Setup took me like 10 to 15 minutes after watching one random YouTube video. It is slower, not gonna lie, but no limits and I do not have to worry about uploading private stuff. I also tried a bunch of other tools people hype on Twitter. Some were decent for one or two uses, then just annoying. Either too slow or randomly restricted. Right now this setup covers almost everything I actually do day to day. I still hit limits sometimes, but it is way less frustrating compared to before. I was paying around 60 to 80 dollars earlier. Now it is basically zero, and I am not really missing much for the kind of work I do. I made a full list of all 11 things I tested and what actually worked vs what was overhyped. Did not want to dump everything here. submitted by /u/Akshat_srivastava_1 [link] [comments]

Google has published its new open-weight model Gemma 4. And made it commercially available under Apache 2.0 License

The model is also available here: 🤗 HuggingFace: https://huggingface.co/collections/google/gemma-4 🦙 Ollama: https://ollama.com/library/gemma4 submitted by /u/BankApprehensive7612 [link] [comments]

How to integrate VS Code with Ollama for local AI assistance

If you’re starting your journey as a programmer and want to jump-start that process, you might be interested in taking The post How to integrate VS Code with Ollama for local AI assistance appeared first on The New Stack.

Ollama taps Apple’s MLX framework to make local AI models faster on Macs

Running large language models (LLMs) locally has often meant accepting slower speeds and tighter memory limits. Ollama’s latest update, built The post Ollama taps Apple’s MLX framework to make local AI models faster on Macs appeared first on The New Stack.