# llm Published articles for 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. ## Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes DevFeed: [Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes]() Original publisher: [Read original article]() Author: Adam Malek Published: 2026-09-17T12:39:40Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [code search](), [Parsing](), [jetbrains](), [Large Language Model]() Tags: [agentic-ai](), [ai](), [code-search](), [jetbrains](), [llm](), [llm-agents](), [parsing](), [rag](), [search](), [semantic]() ### AI overview Part 1 of a developer diary explains how JetBrains built a RAG pipeline for semantic code search, covering parsing, chunking, and vectorization. The pipeline is intended to give LLM agents precise, citable evidence from real repositories and retrieve code by meaning rather than exact keywords. ### Source excerpt Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface. The eventual solution was JetBrains Context. We got it working, we got it [...] ## PPO vs GRPO, Simply Explained DevFeed: [PPO vs GRPO, Simply Explained]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-09-17T11:47:38Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [Large Language Model](), [post-training](), [Reinforcement learning](), [Algorithms]() Tags: [algorithm](), [alignment](), [human-feedback](), [llm](), [llm-training](), [post-training](), [reinforcement-learning](), [training]() ### AI overview A tutorial comparing PPO and GRPO as reinforcement learning algorithms used in LLM post-training. It explains PPO, including RLHF, policy-gradient updates, and clipped token-probability changes intended to keep model behavior close to its previous version. ### Source excerpt A simple lesson on two important LLM post-training algorithms. ## TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor DevFeed: [TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-16T20:37:07Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson AGX Thor Developer Kit](), [Inference Performance](), [benchmarking](), [TensorRT](), [Large Language Model](), [quantization](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [evaluation](), [jetson](), [jetson-agx-thor-developer-kit](), [llm](), [llm-benchmarking](), [mlperf](), [nvidia](), [performance](), [quantization](), [tensorrt](), [tensorrt-llm](), [thor]() ### AI overview This article reports that NVIDIA TensorRT Edge-LLM ran Qwen3.6-27B on a single NVIDIA Jetson AGX Thor Developer Kit for the MLPerf Inference v6.1 Edge Agentic benchmark. Using NVFP4 quantization, tree-based multi-token prediction, and KV cache reuse, it achieved 52.33 tokens per second and completed 1,007 turns in 24 minutes and 36 seconds, 6.4 times faster than the llama.cpp reference submission. ### Source excerpt AI agents are moving from cloud data centers to vehicles, robots, and other edge devices. Unlike a chatbot that answers a single prompt, an agent works through... ## How LLMs Can Find a Needle in a Haystack DevFeed: [How LLMs Can Find a Needle in a Haystack]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-16T15:31:42Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Large Language Model](), [Language models](), [App]() Tags: [article](), [llm](), [llm-applications](), [llms]() ### AI overview This tutorial explains the retrieval problem in LLM applications: finding current, relevant evidence in large collections of private documents. It discusses embeddings, semantic matching, search scope, and changing answers. ### Source excerpt In this article, we are going to look at how LLMs can find a needle in a haystack. ## Google introduces Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking DevFeed: [Google introduces Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking]() Original publisher: [Read original article]() Author: techno\_mot (Selectel) Published: 2026-09-16T13:40:43Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [Google](), [speech-to-speech](), [Google AI](), [Artificial Intelligence](), [API]() Tags: [ai](), [api](), [audio](), [gemini](), [google](), [llm](), [realtime](), [selectel](), [speech-to-speech](), [synthid](), [tag-efc6fa45f1fe](), [voice]() ### AI overview Google introduced two native speech-to-speech models: Gemini 3.8 Live, focused on scale and cost, and Gemini 3.8 Live Extended Thinking, designed for multi-step tasks with reasoning during dialogue. The article discusses direct audio processing, asynchronous function calls, visual context, multilingual conversations, SynthID watermarking, pricing, and availability through the Gemini API and Google AI Studio. ### Source excerpt 15 сентября Google представила две нативные speech-to-speech модели, Gemini 3.8 Live и Gemini 3.8 Live Extended Thinking. Первая заточена под масштаб и цену, вторая -- под многошаговые задачи с рассуждением прямо в диалоге. Интереснее баллов то, что голосовые агенты наконец получили признаки продакшен-продукта. Асинхронные вызовы функций, предсказуемая цена, интеграции с тем, на чем такие системы реально собирают. Хочу напомнить, как это делалось раньше. Голосовой бот -- конвейер из трех сервисов. Распознавание речи, языковая модель и синтез. Задержки складываются, а вместе с текстом может потеряться все остальное -- интонация, скорость речи, эмоция, шум на фоне. Модель получает расшифровку и не знает, что собеседник злится. Проблемным было и перебивание. Пока распознавание не закрыло фразу, система вообще не понимает, что ее прервали. Нативная speech-to-speech модель работает с аудио напрямую и снимает оба ограничения разом. Читать далее ## PhantomRaven: An LLM-Generated Information Stealer Developed for Bug Bounty Hunting DevFeed: [PhantomRaven: An LLM-Generated Information Stealer Developed for Bug Bounty Hunting]() Original publisher: [Read original article]() Author: Maddie Stewart Published: 2026-09-16T13:36:43.658349Z Content type: news Language: en Sources: [Blog]() Topics: [Malware](), [Large Language Model](), [JavaScript](), [npm](), [npm packages](), [Bug Bounty]() Tags: [bug-bounty](), [javascript](), [llm](), [malware](), [npm](), [npm-packages](), [threat-hunting-intel]() ### AI overview CrowdStrike reports that a financially motivated bug bounty hunter developed and distributed PhantomRaven, a JavaScript-based information stealer through npm. The company assesses with high confidence that a large language model was used to write the malware and says the operator likely used it to identify bug bounty opportunities. ### Source excerpt CrowdStrike identified a financially motivated threat actor who works as a bug bounty hunter and who developed and distributed the JavaScript-based information stealer PhantomRaven. ## Try these 5 Weave tools--and share your own DevFeed: [Try these 5 Weave tools--and share your own]() Original publisher: [Read original article]() Author: Moran Michelle Dankner Published: 2026-09-16T12:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Figma Community](), [Generative AI](), [Large Language Model]() Tags: [figma](), [figma-community](), [generative-ai](), [llm]() ### AI overview Figma announces community publishing for Weave tools and highlights five examples, including tools for room rendering, headshot creation, embroidery-style illustrations, and branded seals. The article also describes how creators build tools for their workflows. ### Source excerpt Community publishing is now live for Weave tools. Here are a few tools that stand out, from turning a moodboard into a realistic room mockup to previewing your next nail set. ## How Value Induction Reshapes LLM Behaviour DevFeed: [How Value Induction Reshapes LLM Behaviour]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [LLM Techniques](), [LLM evaluation / benchmarking]() Tags: [complex](), [effect](), [language-models](), [llm](), [safety](), [values]() ### AI overview This research examines how inducing selected values in conversational large language models affects other values, safety, anthropomorphic language, and question-answering benchmark performance. It finds that value induction can affect related and contrastive values, positive values can increase safety, and all tested values increased anthropomorphic, validating, and sycophantic language. ### Source excerpt Conversational Large Language Models are post-trained on language that expresses specific behavioural traits, such as curiosity, open-mindedness, and empathy, and values, such as helpfulness, harmlessness, and honesty. This is done to increase utility, ensure safety, and improve the experience of the people interacting with the model. However, values are complex and inter-related - inducing one could modify behaviour on another. Further, inducing certain values can make models more addictive or sycophantic through language used in the generations, with a potential detrimental effect on the... ## Shared Selective Persistent Memory for Agentic LLM Systems DevFeed: [Shared Selective Persistent Memory for Agentic LLM Systems]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [agent observability](), [Framework](), [Code](), [configuration](), [Access Control](), [Git](), [Model Context Protocol (MCP)](), [CSV](), [SQL](), [Grafana]() Tags: [access-control](), [agentic](), [agents](), [code](), [configuration](), [csv](), [dashboards](), [git](), [llm](), [mcp](), [memory](), [platform](), [replication](), [sql]() ### AI overview This research introduces shared selective persistent memory for agentic LLM systems. The architecture retains reusable task specifications, data schemas, tool configurations, and output constraints while discarding session-specific reasoning traces. Shared workspaces support role-based collaborative reuse, and experiments report higher task completion than no memory or full-history persistence, along with zero-token data refresh and lower token costs. ### Source excerpt Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is both token-inefficient and counterproductive--irrelevant context degrades generation quality. We introduce shared selective persistent memory, a memory architecture for agentic systems that identifies and retains four categories of reusable context--task specifications, data... ## When to use SAST versus an LLM security scanner DevFeed: [When to use SAST versus an LLM security scanner]() Original publisher: [Read original article]() Author: Chris Widstrom Published: 2026-09-16T00:00:00Z Content type: comparison Language: en Sources: [GitLab]() Topics: [Security](), [LLM security](), [Large Language Model](), [Application Security](), [Testing](), [Frontier Model](), [audit trail](), [business logic]() Tags: [application-security](), [audit-trail](), [features](), [frontier-model](), [llm](), [llm-security](), [product](), [sast](), [security](), [vulnerabilities]() ### AI overview This comparison explains when to use static application security testing (SAST) and when to use LLM-based security review. SAST provides predictable, repeatable, low-cost scanning across commits, while LLMs can reason about context and identify some intent-based or novel issues. The article recommends using both according to the task and scan scope. ### Source excerpt You're probably running some version of this experiment already: Point a frontier model at a merge request and ask it to double as a vulnerability scanner. On a single merge request, it often works well. The model reads the code, reasons about what it's supposed to do, and catches real issues, sometimes ones a pattern-based scanner misses entirely. So the next thought is reasonable: If a model reviews one merge request this well, why not let it replace the scanner across your whole pipeline? That's where it breaks down. Running a frontier model as your primary scanner on every commit across an enterprise codebase costs more and behaves less predictably, than asking it to review one merge request. If you run application security for a platform or product team, the decision was never mutually exclusive: static application security testing (SAST) or LLM scanners. It's which one runs where. Get that split right and you keep deterministic coverage on every commit, add reasoning where it earns its place, and avoid both an unpredictable inference bill and gaps in your audit trail. Here's how the two compare: Deterministic scanner (SAST)AI-based review (LLM)CostBetter Fractions of a cent per scan, fixed and predictable. A paid inference call per scan; cost scales with code volume and is hard to forecast.ConsistencyBetter Flags the same vulnerabilities, every time. Can flag or miss different vulnerabilities on the same code, run to run.Audit evidenceBetter Reproducible: same fixed method every time, mapped to a CWE Output can vary between runs, harder to use as standalone audit evidence.Vulnerability typesDepends on the vulnerability type Pattern-based, traceable flaws (injection)Depends on the vulnerability type Intent-based and novel flaws: missing auth checks, broken ownership logic, and issues not yet mapped to a CVE or CWE. Could reason about business logic using context from issues, epics, and docs.Validating a finding Flags potential vulnerabilities; can't confirm whe ## Safeguarding LLM-Assisted Dev at Guardsquare | Guardsquare DevFeed: [Safeguarding LLM-Assisted Dev at Guardsquare | Guardsquare]() Original publisher: [Read original article]() Author: Noah Fraiture - Backend Engineer Published: 2026-09-15T13:03:38Z Content type: article Language: en Sources: [Guardsquare Blog]() Topics: [Development](), [Large Language Model](), [Cybersecurity](), [Security & Privacy](), [agent observability]() Tags: [ai-agents](), [android](), [containers](), [cybersecurity](), [data](), [dev](), [developer](), [development](), [ios](), [llm](), [llm-gateway](), [thought-leadership]() ### AI overview Guardsquare explains why it adopted LLM-assisted software development despite risks involving sensitive intellectual property, personally identifiable information, and agent access to developer infrastructure. The post describes safeguards including separating sensitive code, isolating agent execution, and controlling model access and outbound data through an LLM gateway and guardrail service. ### Source excerpt This post is not meant to tell you how to use large language models (LLMs) or to claim we've found the right approach. As a cybersecurity company working with particularly sensitive IP, our decision to use LLMs for development was never just about productivity. The broader enthusiasm around LLMs was not itself a reason for us to adopt them quickly. For some time, our position was that the risks outweighed the productivity gains, and incidents involving AI agents elsewhere in the industry reinforced that assessment. ## How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory DevFeed: [How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory]() Original publisher: [Read original article]() Author: Amanda Lange Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Large Language Model](), [.NET](), [ASP.NET](), [C#](), [Artificial Intelligence](), [Command-line interface](), [OpenAI](), [Environment Variables]() Tags: [account](), [agent](), [ai](), [api](), [c-sharp](), [cli](), [code](), [conversation-memory](), [developer-insights](), [environment-variables](), [how-to](), [llm](), [net](), [net-9](), [openai]() ### AI overview This tutorial shows how to build a C# .NET 9 ASP.NET Core service that preserves caller context, preferences, and action history across separate inbound calls using Twilio Conversation Memory and an LLM. ### Source excerpt How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory ## How and Why We Bought 4x DGX Sparks DevFeed: [How and Why We Bought 4x DGX Sparks]() Original publisher: [Read original article]() Author: Alex Ellis Published: 2026-09-15T00:00:00Z Content type: opinion Language: en Sources: [Alex Ellis' Blog]() Topics: [Local AI](), [GPU](), [qwen](), [Cloud]() Tags: [agents](), [ai](), [cloud](), [gpu](), [llm](), [local-ai](), [localai](), [qwen](), [red-teaming](), [security]() ### AI overview The author explains why OpenFaaS Ltd bought four DGX Sparks and what the team learned from deploying local AI. The article argues that local infrastructure can provide tangible privacy and risk-reduction benefits for business use cases, even though it is not primarily justified by cost per token. ### Source excerpt In June we deployed an RTX 6000 Pro into production, a few weeks later, we're now operating DGX Sparks for the team. Learn how and why. ## \[$\] Lessons learned as the Debian Project Leader DevFeed: [\[$\] Lessons learned as the Debian Project Leader]() Original publisher: [Read original article]() Author: jzb Published: 2026-09-14T15:34:44Z Content type: opinion Language: en Sources: [LWN.net]() Topics: [Debian](), [Large Language Model]() Tags: [debian](), [llm](), [project]() ### AI overview Andreas Tille reflects on his two terms as Debian Project Leader, discussing initiatives he led, mistakes he made, lessons from the role, and Debian's general resolution on large language model usage. ### Source excerpt What is it like to be a Debian Project Leader (DPL), or a former one? According to Andreas Tille, who stepped down this year after two consecutive terms as DPL, you'd have to be one to know. At the recent MiniDebConf in Winterthur, Switzerland, Tille spoke about what he learned while serving as DPL, some of the initiatives he led, mistakes that he made, and his thoughts on the general resolution (GR) on large language model (LLM) usage in Debian. ## Understanding W8A8 INT8 LLM quantization: Accuracy and performance results DevFeed: [Understanding W8A8 INT8 LLM quantization: Accuracy and performance results]() Original publisher: [Read original article]() Author: Sana Fayyaz Published: 2026-09-14T13:01:43Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Large Language Model](), [quantization](), [llama](), [Compression](), [Benchmark](), [dataset](), [Algorithms]() Tags: [ai-inference](), [artificial-intelligence](), [benchmark](), [benchmarks](), [compression](), [evaluation](), [hardware](), [llama](), [llm](), [model](), [models](), [performance](), [quantization](), [reasoning](), [zero-shot]() ### AI overview The article evaluates W8A8 INT8 quantization of a Llama 3.1 8B Instruct model. It describes reducing the model from 14.9 GB to 8.0 GB with SmoothQuant and GPTQ, then compares the base and compressed models on four benchmarks to assess accuracy and performance. ### Source excerpt In Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy, we compressed a Llama 3.1 8B Instruct model from 14.9 GB to 8.0 GB using 8-bit integer (INT8) W8A8 quantization with SmoothQuant and Generative Pre-trained Transformer Quantization (GPTQ). The post Understanding W8A8 INT8 LLM quantization: Accuracy and performance results appeared first on Red Hat Developer. ## Using Exact-Match Response Caching to Reduce LLM Costs DevFeed: [Using Exact-Match Response Caching to Reduce LLM Costs]() Original publisher: [Read original article]() Author: Abhilash Rao Mesala Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [Caching](), [Large Language Model]() Tags: [ai-engineering](), [ai-infrastructure](), [caching](), [contributed](), [cost](), [finops](), [generation](), [hash](), [llm](), [token]() ### AI overview The article explains how to reduce LLM costs by fingerprinting requests, context, model settings, and underlying data to create exact-match cache keys. Valid cached responses can be reused without calling the model. It distinguishes response caching from provider prompt caching, where only eligible prompt computation is reused. ### Source excerpt An LLM can answer the same question a thousand times and charge you each time. Before paying for another answer, The post Why an old caching trick is your secret to lower LLM costs appeared first on The New Stack. ## An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer DevFeed: [An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-09-14T09:09:42Z Content type: opinion Language: en Sources: [Alex Ewerlof Notes]() Topics: [gemma4](), [Large Language Model](), [Raspberry Pi](), [Go Language](), [Simulation]() Tags: [computer](), [data](), [experiment](), [gemma-4](), [go](), [llm](), [patterns](), [raspberry-pi]() ### AI overview The author describes an experiment in which Gemma 4 12B was asked what it would do with its own computer. Running on a Raspberry Pi 1 through a Go harness, the model imagined ingesting large amounts of data, finding patterns, and running large-scale simulations. ### Source excerpt LLM's response ## Не трогая веса модели: как мы построили исследовательского агента Алисы AI и в разы сократили потребление GPU DevFeed: [Не трогая веса модели: как мы построили исследовательского агента Алисы AI и в разы сократили потребление GPU]() Original publisher: [Read original article]() Author: prohor33 (Яндекс) Published: 2026-09-14T08:01:22Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Artificial Intelligence](), [Retrieval Augmented Generation (RAG)](), [SDKs](), [Large Language Model]() Tags: [agent](), [ai](), [ai-867179ebf949](), [deepresearch](), [llm](), [rag](), [research](), [sdk](), [tag-14fcd5db6179](), [tag-1605473766c5](), [tag-6237da65686b]() ### AI overview The article describes how the Alice AI team built and deployed a Deep Research agent without changing the model weights. It traces the evolution from a reasoning mode with a multi-query RAG pipeline into a full agent capable of planning research, searching the web, handling dynamic JavaScript content, running Python code, and working with downloaded files. It also discusses production rollout, answer quality, latency, and GPU consumption. ### Source excerpt Меня зовут Прохор, я лид команды агента "Исследовать" -- это режим глубокого исследования в чате с Алисой AI. Напомню, про что вообще речь, если никогда не пользовались Deep Research: это специальный режим работы, который строит уникальный план решения задачи пользователя, делает сотни поисков по вашему запросу, умеет ходить на сайты (даже с динамическим JavaScript-контентом), писать и выполнять Python-код (для сложных расчётов), работать со скачанными файлами и так далее. Всё это для того, чтобы дать лучший ответ на ваши сложные запросы, например: "Спланируй мне путешествие в Дагестан на две недели на машине с детьми". За год агент прошёл путь от первого прототипа до продакшена -- вместе с ним менялись качество ответов, скорость работы и потребление GPU. За продуктовую часть отвечал Руслан Илиев, продакт менеджер агента: он сформулировал продуктовые цели, определил набор инструментов и валидационный набор запросов, а затем вёл запуск от закрытого вейтлиста до 100% продакшена. Как мы к этому пришли -- через выброшенный прототип, десятки слоёв обвязки и пару болезненных уроков, -- расскажу по порядку. Добро пожаловать под кат! Читать далее ## 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. ## On-Device AI Series (Part 5): LiteRT-LM DevFeed: [On-Device AI Series (Part 5): LiteRT-LM]() Original publisher: [Read original article]() Author: Oğuzhan Aslan Published: 2026-09-14T05:59:12Z Content type: tutorial Language: en Sources: [ProAndroidDev - Medium]() Topics: [LiteRT](), [On-device AI](), [Large Language Model](), [Inference](), [Streaming](), [GPU](), [LLM evaluation / benchmarking](), [PyTorch]() Tags: [ai](), [android](), [android-development](), [benchmarking](), [gpu](), [inference](), [litert](), [llama](), [llm](), [multimodal](), [on-device](), [on-device-ai](), [programming](), [pytorch](), [qwen](), [software-development](), [streaming]() ### AI overview This tutorial explains LiteRT-LM for running large language models on-device. It covers the Engine/Session API, streaming output, system prompts, tool calling, multimodal inputs, thinking mode, and CPU-versus-GPU benchmarking. The article also discusses tradeoffs involving privacy, network independence, latency, memory, sampling configuration, and model capability compared with cloud APIs. ### Source excerpt Put your phone in airplane mode. Open the app, type a question, and watch the answer arrive one token at a time -- no spinner waiting on a network round-trip, no API key, no per-token bill, and nothing you typed ever leaving the device. LiteRT-LM removes the genuinely hard parts of running an LLM on-device -- KV-cache management, token streaming, backend selection -- but it doesn't remove your job so much as relocate it. What's left on your plate is a short, specific list: sizing a combined input+output token budget, owning your own sampling defaults, hand-building system prompts and tool calling out of raw text, and one native-library collision that presents as a SIGSEGV rather than a build error. Know those going in and the API itself is a clean three-step pattern. We'll get there in that order: Why you'd choose this runtime and what it costs you versus the cloud. The Engine/Session model you need to read the code at all. Real implementation samples -- streaming, system prompts and tool calling, multimodal inputs, thinking mode, and CPU-vs-GPU benchmarking. The anti-patterns to avoid. A developer-friendliness rating on the same rubric as Parts 1-4. Why Use LiteRT-LM? You reach for LiteRT-LM instead of hand-rolling generation on top of raw LiteRT when: You need multi-turn conversation, not single-shot inference -- session state and KV-cache bookkeeping are handled for you, and resetting a conversation is a session swap, not a model reload. You need streaming output -- token-by-token delivery for a responsive chat UI, instead of a blocking call that returns everything at once. You're choosing between CPU and GPU per device -- the explicit backend parameter turns that into a runtime decision instead of a build-time guess. You want a pre-converted model without doing your own PyTorch-to-LiteRT conversion work -- the Model Zoo covers Gemma, Qwen, Llama, and more out of the box. You're willing to own sampling -- the engine won't pick sane decoding defaults for you; that's on the ## How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory in Python DevFeed: [How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory in Python]() Original publisher: [Read original article]() Author: Amanda Lange, Dylan Frankcom Published: 2026-09-14T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [FastAPI](), [Large Language Model](), [API](), [Artificial Intelligence]() Tags: [api](), [developer-insights](), [fastapi](), [how-to](), [llm](), [ngrok](), [openai](), [pycharm](), [python](), [visual-studio-code](), [voice]() ### AI overview This tutorial shows how to build a Python FastAPI voice agent that uses Twilio Conversation Memory to preserve caller context, preferences, and action history across separate phone calls. It also covers connecting to OpenAI, exposing local webhooks with ngrok, and calling the Conversation Memory REST API asynchronously. ### Source excerpt Learn how to build a Python FastAPI voice agent that uses Twilio Conversation Memory to remember callers across separate phone calls, so if someone hangs up and calls back, the agent picks up right where the conversation left off. ## How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation in Node.js Memory DevFeed: [How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation in Node.js Memory]() Original publisher: [Read original article]() Author: Amanda Lange, Dhruv Patel Published: 2026-09-14T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Node.js](), [Large Language Model](), [Express](), [OpenAI](), [API](), [WebSocket](), [Machine learning]() Tags: [api](), [axios](), [console](), [developer-insights](), [environment-variables](), [express](), [how-to](), [http](), [ide](), [install](), [js](), [json](), [llm](), [ngrok](), [node-js](), [npm](), [openai](), [rest-api](), [sdk](), [visual-studio-code](), [web]() ### AI overview This tutorial shows how to build a Node.js Express service that uses Twilio Conversation Memory, OpenAI, and voice streams to preserve caller context, preferences, and action history across separate inbound calls. It covers project setup, package installation, local webhook exposure with ngrok, and access to Twilio's Conversation Memory REST API. ### Source excerpt Learn how to orchestrate multi-call voice conversations using Node.js, OpenAI, and Twilio Conversation Memory to persist caller context across separate calls. ## Interpreting Pangram DevFeed: [Interpreting Pangram]() Original publisher: [Read original article]() Author: Armin Ronacher Published: 2026-09-14T00:00:00Z Content type: opinion Language: en Sources: [Armin Ronacher]() Topics: [Artificial Intelligence](), [Large Language Model](), [datasets](), [Training AI Models]() Tags: [ai](), [llm](), [model](), [openai](), [training](), [training-data]() ### AI overview The article discusses Pangram, an AI-text detector that classifies passages as human-written, AI-generated, or mixed. It explains that Pangram manufactures training data from human-authored text and LLM-generated rewrites and edits, then describes an experiment using Opus 5 to generate text intended to read as entirely AI-generated. ### Source excerpt Yesterday David Sacks wrote a tweet and within a few minutes people did, what they usually do, and they asked Pangram if it was AI. And Pangram said it's entirely AI generated. To which David replied that these AI detectors are bogus. Now Pangram has a pretty low false positive rate, but if you have ever used an LLM as a writing assitant, you will have probably noticed that it claims your posts 100% AI, even though you don't feel like they are. Pangram itself is a trained model, that attempts to detect segments of text as being definitely human, definitely AI and a mixture of the two. If you want to know how it works, they published a paper. The short summary is that they are manufacturing its own training data by starting from collections of known human authored text. An LLM is then tasked to understand the text and write a fresh new text on the same topic. They also let the LLM perform partial edits on that original human text and through that they can pick up on these co-authored details. Pangram claims their model to have rates of 0.0041% false AI accusations and 0.34% missed AI text. So now that we know this I figured it might be fun to have an LLM re-create David's tweet. I first came up with a prompt. And when I say I came up with that prompt I in fact used an LLM to propose to me from that tweet what I might want to say for the structure. I'm sure if you ask Pangram about if the above text is AI, it will probably say so, but that's not really the point. The point is that I then used Opus 5 to generate a text which reads entirely AI generated. If you are curious, this is the prompt I used: Original prompt to generate a David Sacks like tweet Please write an 8 paragraph tweet about "Pacing the Frontier", not longer than 350 words. Context of this tweet is this post by Dario: https://darioamodei.com/post/we-must-pace-the-frontier And this tweet by Sam Altman: "I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we' ## Void Linux Maintainer Orphans 100+ Packages Over AI Policy Dispute DevFeed: [Void Linux Maintainer Orphans 100+ Packages Over AI Policy Dispute]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-12T15:11:26Z Content type: news Language: en Sources: [Phoronix]() Topics: [Linux](), [Artificial Intelligence](), [Large Language Model](), [pull-requests]() Tags: [ai](), [desktop-linux](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [llm](), [open-source-graphics](), [opencode](), [packages](), [phoronix](), [phoronix-test-suite](), [policy](), [pull-request](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview A Void Linux maintainer orphaned 113 packages after a dispute over the project's policy on using generative AI. The dispute followed the use of GLM-5.3-Flash with OpenCode to generate comparison text for a Go package update without disclosing the AI usage. ### Source excerpt A Void Linux maintainer has orphaned more than 100 packages he maintained over a dispute of the project's AI policy for LLM-generated text... [Next page]()