# local LLM Published articles for local LLM. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A Daily Digest of What Actually Got Merged, Built from MQTT and a Local LLM DevFeed: [A Daily Digest of What Actually Got Merged, Built from MQTT and a Local LLM]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-09-14T07:00:00Z Content type: article Language: en Sources: [Philipp Lehmann]() Topics: [pull-requests](), [MQTT](), [Large Language Model](), [SQLite]() Tags: [architecture](), [ci-cd](), [llm](), [local-llm](), [mqtt](), [observability](), [pull-requests](), [python](), [sqlite]() ### AI overview This article describes a daily digest that uses MQTT merge events, a SQLite subscriber, and a locally run LLM to summarize merged pull requests by repository without sending diffs to a cloud API. ### Source excerpt paho-mqtt subscriber to SQLite plus a local LLM: a daily digest that summarises merged pull requests per repository from diff stats, no cloud API. ## How I Run My Daily Standup on a Local LLM: Git, Gemma 4, llama.cpp and Telegram DevFeed: [How I Run My Daily Standup on a Local LLM: Git, Gemma 4, llama.cpp and Telegram]() Original publisher: [Read original article]() Author: Ivan Morgillo Published: 2026-05-27T09:51:06Z Content type: tutorial Language: en Sources: [Ivan Morgillo]() Topics: [gemma4](), [llama.cpp](), [Git](), [Large Language Model](), [API](), [Claude]() Tags: [anthropic](), [api](), [claude](), [gemma](), [gemma-4](), [git](), [llama-cpp](), [llm](), [local](), [local-llm](), [sdk](), [telegram]() ### AI overview A developer describes replacing a cloud-based daily standup pipeline with a local Gemma 4 E4B model running on llama.cpp. The pipeline reads Git commits, formats them, and posts the summary to Telegram. The article also explains how empty API-key settings caused several automations to use personal Claude subscription authentication instead of the Anthropic API. ### Source excerpt A real-world pipeline that scans my Git repos every morning, formats the day's commits with a local Gemma 4 E4B on llama.cpp, and posts the summary to Telegram -- no cloud, no API key, no AUP warnings. ## Alternatives for the EDIT tool of LLM agents DevFeed: [Alternatives for the EDIT tool of LLM agents]() Original publisher: [Read original article]() Published: 2026-05-19T07:26:03Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Tool](), [Local AI](), [Large Language Model]() Tags: [agents](), [alternatives](), [inference](), [llm](), [local-llm](), [tool]() ### AI overview The article proposes a tag-based EDIT tool for LLM agents that preserves check-and-set semantics while reducing the tokens needed to repeat old text. It uses short line checksums alongside line numbers so edits can be validated without reproducing the original content. ### Source excerpt EDIT: of course this was already done in the past! I had little doubts but people just confirmed me about it on Twitter :) But, keep reading: the CRC32 compromise at the end is an interesting tradeoff, and this is a good discussion to have in general. Right now I'm working to an agent for my DS4 project. Local inference is token-poor, it's a battlefield where optimizations count. I was quite surprised by the fact the EDIT tool everybody is using right now forces the LLM to emit the old version of the text verbatim. This CAS (check and set) mode of operation, where I say EDIT old="foo" new="bar", is needed because there are often colliding edits (the user is editing as well, or checked out a different branch, and so forth) and because the LLM can just hallucinate that a given line had a given content. This means, basically, that just using line numbers is very fragile: to say, change line 22 with new="foobar" is not good. Yet I don't want my local LLM to throw away tokens rewriting the old text each time, also because certain times the old text has a lot of special chars and spaces that the model may get wrong; in this case the tool would fail, forcing the LLM to do the same edit again. So I (re)designed a tag-based EDIT tool that is still CAS style, but more tokens efficient. The READ and SEARCH tools return something like that: 10:Q8fA int count = 10; 11:rA3_ if (count > limit) { 12:Kq9z count = limit; 13:PX0b } So there are line numbers and tags. The tag is 4 chars, on average 2.5 LLM tokens, representing a checksum of the line. Now the LLM can edit like this: { "tool": "edit", "path": "/tmp/example.c", "line": 10, "tag": "Q8fA", "new": "int count = 11;" } Or, multi line, like this: { "tool": "edit", "path": "/tmp/example.c", "lines": "11:rA3_\n12:Kq9z\n13:PX0b", "new": "if (count > limit)\n return limit;" } The saving is significant especially when the agent is deleting big amounts of text, but also in the general case. However, there is some overhead due to the ## Local LLM Inference : llama.cpp, GGUF, Quantizations and GGML Explained DevFeed: [Local LLM Inference : llama.cpp, GGUF, Quantizations and GGML Explained]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-03-03T11:31:04Z Content type: tutorial Language: en Sources: [Neural Bits]() Topics: [llama.cpp](), [ggml](), [Inference](), [Large Language Model](), [Local AI](), [quantization]() Tags: [backend](), [cross-platform](), [efficiently](), [embedded](), [format](), [ggml](), [inference](), [llama-cpp](), [llm](), [local-llm](), [model]() ### AI overview A practical guide to local LLM inference with llama.cpp, explaining how the GGUF model format, GGML backend concepts, quantization, and inference workflows fit together for efficient execution on edge devices. ### Source excerpt Learn how the llama.cpp runtime, GGML backend concepts, and GGUF model format fit together for fast local inference across devices. ## The Complete Guide to Ollama: Local LLM Inference Made Simple DevFeed: [The Complete Guide to Ollama: Local LLM Inference Made Simple]() Original publisher: [Read original article]() Author: The AI Merge Published: 2025-10-25T13:02:36Z Content type: tutorial Language: en Sources: [Neural Bits]() Topics: [Ollama](), [Inference](), [Large Language Model](), [Command-line interface](), [Docker](), [Python](), [API]() Tags: [api](), [cli](), [docker](), [inference](), [llm](), [local](), [local-llm](), [ollama](), [python]() ### AI overview A practical guide to Ollama for local large language model inference. It covers Ollama's role in the LLM ecosystem, its architecture, model management and customization, Hugging Face model integration, an OpenAI-compatible API, Python usage, and Docker deployment. ### Source excerpt A deep dive into Ollama's architecture, going through model management, OpenAI API schema and local inference integrations with CLI, Docker and Python. ## Structured Output with LangChain and Llamafile DevFeed: [Structured Output with LangChain and Llamafile]() Original publisher: [Read original article]() Author: brakmic Published: 2025-06-22T16:50:12Z Content type: tutorial Language: en Sources: [Harris Brakmic - Coding]() Topics: [LangChain](), [llamafile](), [JSON](), [llama.cpp](), [Parser](), [Pydantic]() Tags: [ai](), [code](), [coding](), [cpp](), [how-to](), [import](), [json](), [langchain](), [llama-cpp](), [llamafile](), [llm](), [local](), [local-llm](), [models](), [properties](), [python](), [structured-output]() ### AI overview A tutorial on using LangChain with Llamafile to produce structured JSON output from a local large language model. It uses JsonOutputParser, PromptTemplate, and a Pydantic model because Llamafile does not provide a with_structured_output method. ### Source excerpt Learn how to extend Llamafile with LangChain's JsonParser to produce clean, structured JSON output