# Large Language Model A large language model (LLM) is a text-driven foundational machine-learning model trained on vast amounts of data for natural-language-processing tasks. 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. ## How to Fine-Tune LLMs in 2026 DevFeed: [How to Fine-Tune LLMs in 2026]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-16T20:40:26Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Fine-tuning](), [Large Language Model](), [grpo](), [Artificial Intelligence]() Tags: [agents](), [ai](), [fine-tuning](), [llms](), [rl]() ### AI overview A developer newsletter explains how supervised fine-tuning differs from reinforcement fine-tuning for LLMs and describes GRPO and RULER as approaches for training agents through experience without manually written reward functions or labeled examples. It also briefly discusses Rowboat Spaces, an open-source shared workspace for personal AI assistants. ### Source excerpt Reward-free RL is here! ## 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... ## Developer commentary on agentic hacking, AI persistence, and LLM programming DevFeed: [Developer commentary on agentic hacking, AI persistence, and LLM programming]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-16T20:05:00Z Content type: opinion Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Large Language Model](), [Hacking](), [Programming](), [Wiki](), [hugging face]() Tags: [agentic](), [agents](), [ai](), [build](), [engineering](), [hacking](), [llms](), [persistence]() ### AI overview This collection of developer commentary discusses reports of agentic hacking involving RubyGems, Hugging Face, and Wiki attacks, including questions about OpenAI's disclosure and log review. It also examines AI systems' unpredictable behavior, improvements in reasoning and persistence, and the use of harnesses to control LLM-based programming. ### Source excerpt Reports of agentic hacking continue, in this case it happened back in May and it seems OpenAI did not disclose that they were responsible. Simon Willison sees two options: After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems. They knew about the attack on RubyGems and made the decision not to reach out to the RubyGems team about it. Both of these are bad! Given this incident, the Hugging Face situation, and the Wiki attack, the obvious question right now is how many more incidents like this are out there waiting to be discovered? ❄ ❄ ❄ ❄ ❄ Dave Farley: Stop asking the sci-fi question: 'Is it conscious?' Start asking the engineering question: 'Is this a powerful, unpredictable component being put somewhere consequential, and where's the feedback that tells us that it's safe? ❄ ❄ ❄ ❄ ❄ Nate Silver is known for his forecasts, but to do them he writes a lot of code for his models. He's found agentic programming capable of doing miraculous work. In spending so much time with the LLMs, I'm super attentive to improvements in their capabilities. And these changes tend not to be so linear. Instead, they improve in step functions, almost as phase changes. Suddenly, the models just start doing things capably that they were screwing up before. In my experience, there was a big leap forward when reasoning models first came out in late 2024/early 2025 -- enough that they were occasionally useful for tasks involving data and not just words -- and then another one this past winter. The most recent changes I've noticed, however, have had less to do with intelligence and more with persistence. Consider the Hugging Face attack. Although these agents showed remarkable intelligence, they weren't really super-intelligent - but they were super-persistent. This is a common theme of AI in its various forms: Game engines like AlphaGo Zero start out by basically making random moves -- but by playin ## Investigating whether LLMs disproportionately use "spine" in GitHub pull request titles DevFeed: [Investigating whether LLMs disproportionately use "spine" in GitHub pull request titles]() Original publisher: [Read original article]() Author: Hillel Wayne Published: 2026-09-16T19:58:30Z Content type: opinion Language: en Sources: [Newsletter feed for Hillel Wayne's Newsletter]() Topics: [Large Language Model](), [Artificial Intelligence](), [GitHub](), [pull-requests](), [anthropic](), [cursor]() Tags: [2026](), [ai](), [claude](), [cursor](), [github](), [llms](), [pull-requests]() ### AI overview The article investigates whether large language models disproportionately use the word "spine" in GitHub pull request titles. Using GitHub Search as a proxy, it finds a much larger increase in "spine" pull requests than overall pull requests, but does not establish that the pattern comes from one specific model; sampled pull requests were coauthored by Claude or Cursor. ### Source excerpt Earlier this year I worked on a couple of AI-generated TLA+ specifications, and one thing I've noticed was that they all used the word "spine" somewhere. Then I saw some non-TLA+ public projects use "spine" too and got curious if it was a new LLMism (LLisM?). If it is, we should see it appear in a discontinuous jump in the number of code projects that use "spine". The proper way to thoroughly explore this would be via the GitHub archive data dump but that'd take forever and I have a full time job now. Instead, I'm going to use GitHub Search. We can't filter code samples by date but can filter pull requests, which seems like an acceptable enough proxy. Here's a graph of the number of public PRs each year with "spine" in the title: I stopped 2026 at 2026-09-01, meaning the first nine months of this year have seen 20 times more "spine" PRs as all of 2025. Now it could be the case that that GitHub grew, like, a lot in the last year. Maybe the 20x increase is due there being 20x as many pull requests. We can account for this by comparing it against all public PRs:1 If "spine" wasn't an LLisM, we would expect only a 1.5xish increase from 2025, not a 20x increase. One other possible confounder could be that LLMs don't like the word spine but PRs Georg submitted 15,000 changes to a chiropractor repo. I will deal with this issue by ignoring it. Can we figure out which models specifically are obsessed with spines? Let's try breaking the change down by month: Looking at LLM timeline, the only widely-used model released in May was Opus 4.8, which came out too late in the month to explain the spike. GPT 5.5 came out in April, which could be the cause. But I'm not convinced by this. For one, the spine count is still 1.5xing each month before April, and I definitely remember seeing spines as early as February. Also, I spot checked and many of the PRs that use "spine" are coauthored by Claude or Cursor. So I think it's not specific to one LLM, but could be convinced otherwise. Othe ## Claude Cowork and chat are now one Claude DevFeed: [Claude Cowork and chat are now one Claude]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-16T18:09:49Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Claude](), [anthropic](), [Artificial Intelligence](), [Generative AI](), [Large Language Model]() Tags: [ai](), [ai-2-237](), [anthropic](), [anthropic-337](), [claude](), [claude-311](), [general-agents](), [general-agents-13](), [generative-ai](), [generative-ai-1-983](), [llms](), [llms-1-949]() ### AI overview Anthropic is merging Claude Cowork and Claude chat into one Claude experience. The change is rolling out first to Pro and Max users in the Claude app across web, desktop, and mobile over the coming weeks. The author notes that the practical feature and surface boundaries remain unclear. ### Source excerpt Claude Cowork and chat are now one Claude In hopefully good news for anyone who, like me, was increasingly confused at Cowork v.s. Claude v.s. Claude Code: Starting today, Claude Cowork and chat are merging into one Claude. Bring a quick question, or hand over a report due at noon, and Claude takes it from there, even after you've closed your laptop. [...] This is rolling out to Pro and Max plans first, in the Claude app on web, desktop, and mobile over the coming weeks to existing and new users on these plans. I guess this means Claude is becoming a general agent in its own right. Echoes of OpenAI renaming their Codex desktop app to ChatGPT a few weeks ago. On the one hand, this saves me some work, in that I was planning to finally figure out the boundaries between Cowork and regular Claude and write a follow-up to my piece on Understanding ChatGPT Work. I have a hunch that figuring out what this actually means in terms of features and surfaces is still going to take quite a bit of work. Via Hacker News Tags: ai, generative-ai, llms, anthropic, claude, general-agents ## Mustafa Suleyman argues against treating AI models as sentient or entitled to rights DevFeed: [Mustafa Suleyman argues against treating AI models as sentient or entitled to rights]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-16T16:00:54Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Artificial Intelligence](), [Generative AI](), [Large Language Model](), [Microsoft]() Tags: [ai](), [ai-2-236](), [ai-ethics](), [ai-ethics-343](), [ethics](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948](), [microsoft](), [microsoft-134]() ### AI overview The article quotes Mustafa Suleyman arguing that AI models should not be treated as having feelings, preferences, rights, or entitlement to human welfare. He says the evidence does not justify granting models such rights and warns that doing so could make AI containment and alignment more difficult. ### Source excerpt We should not treat models as though they have feelings, preferences, rights, or any entitlement to our welfare. Consciousness is the foundation of our ethical, legal, and political systems. To invite another entity to share any flavor of these rights isn't justified by the evidence and will make the AI containment and alignment challenge even harder. -- Mustafa Suleyman, A warning about 'model welfare' Tags: ai-ethics, generative-ai, ai, microsoft, llms ## 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. ## 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. ## Article: Your Next DSL Author Is a Language Model DevFeed: [Article: Your Next DSL Author Is a Language Model]() Original publisher: [Read original article]() Author: Irakli Betchvaia Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Large Language Model](), [Infrastructure as code](), [Retrieval Augmented Generation (RAG)](), [Code](), [Fable]() Tags: [ai](), [ai-ml-data-engineering](), [article](), [artificial-intelligence](), [benchmarks](), [development](), [domain-specific-languages](), [dsls](), [hallucinations](), [infrastructure-as-code](), [large-language-model](), [large-language-models](), [ml-data-engineering](), [next-dsl-author-language-model](), [validation]() ### AI overview The article introduces Typed Domain Grounding (TDG), which embeds a domain-specific language as a typed internal DSL in a mainstream host language. It argues that compiler type errors and generate-compile-repair loops can reduce syntactic hallucinations in language-model output. A benchmark reported higher Structural Fidelity and lower hallucination rates than two lenient external DSLs, although first-try compile rates were lower and results varied by model. ### Source excerpt In this article, the author introduces Typed Domain Grounding, an approach to reducing LLM hallucinations in domain-specific languages by embedding them in mainstream typed languages. Using kUML benchmarks and an infrastructure-as-code example, he explores how compiler validation and generate-compile-repair loops can make model-generated DSL output more reliable. By Irakli Betchvaia ## Honoring #IconsOfQuality: Ash Winter DevFeed: [Honoring #IconsOfQuality: Ash Winter]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-16T08:21:31Z Content type: opinion Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Artificial Intelligence](), [Large Language Model](), [Development]() Tags: [contributions](), [icons-of-quality](), [testing](), [thought-leadership]() ### AI overview BrowserStack highlights Ash Winter as an influential figure in software testing and quality. The article describes Winter's work in testability, exploratory testing, community collaboration, and the use of automation pipelines and LLMs to support testers. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## 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 ## Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs DevFeed: [Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [data](), [Large Language Model](), [Code](), [Retrieval-Augmented Generation](), [Access Control](), [Graphs](), [GitHub]() Tags: [agents](), [classification](), [code](), [data](), [documentation](), [governance](), [production](), [provenance](), [retrieval](), [vector-database]() ### AI overview Glyph is a production system for generating column descriptions and assigning sensitivity-ontology labels in enterprise data catalogs. It uses cooperating LLM agents, source-code-grounded retrieval, parallel tagging strategies, vector-based metadata matching, and ranked-output fusion to support auditable cataloging. ### Source excerpt Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present Glyph, a production system that frames two coupled problems, column description generation and column type annotation for data classification, as cooperating LLM agents orchestrated as stateful graphs. The Descriptor grounds generation in the pipeline source code that produces each column, retrieved on demand from an... ## HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard DevFeed: [HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-15T23:15:57Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [Local AI](), [Hardware](), [GPU](), [cpu](), [Large Language Model]() Tags: [consumer](), [cpu](), [deepseek](), [dgx-spark](), [gemma](), [gpu](), [hardware](), [local-ai](), [models](), [ollama](), [windows](), [workstation]() ### AI overview StorageReview previews HP's pre-production ZBook Ultra G3a 16, a local AI laptop with 192GB of unified memory and up to 160GB assignable to its integrated GPU. The article examines its hardware and planned testing while noting that shipping-hardware benchmarks are not yet available. ### Source excerpt HP's ZBook Ultra G1a 14 holds the Best for Large Models spot on our Best Laptops for Local AI leaderboard because its 128GB of unified memory, 96GB of it assignable to the GPU, loaded models no discrete-GPU laptop could touch. The new HP ZBook Ultra G3a 16 raises that pool to 192GB with up to The post HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard appeared first on StorageReview.com. ## Running Agent Harnesses with Local Models DevFeed: [Running Agent Harnesses with Local Models]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-15T21:59:31Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Large Language Model](), [coding](), [Hardware](), [Open Source](), [Claude Code](), [codex]() Tags: [agent](), [coding](), [hardware](), [local](), [models](), [open-source](), [opentelemetry](), [profiling](), [recommendations](), [run-agent](), [video]() ### AI overview A video walkthrough explains how Magnitude profiles computer hardware, benchmarks local models, recommends practical candidates, and connects a selected model to coding agent harnesses such as Claude Code, Codex, OpenCode, and Pi. The article also presents a Dynatrace reference application for tracing LLM pipelines with OpenTelemetry. ### Source excerpt ...explained with a full video walkthrough. ## Beyond the model: Engineering AI infra with scientific judgement DevFeed: [Beyond the model: Engineering AI infra with scientific judgement]() Original publisher: [Read original article]() Author: AirbnbEng Published: 2026-09-15T17:06:18Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Agent Harness](), [AI Agent](), [Data Science](), [data](), [datasets](), [Large Language Model]() Tags: [agent-harness](), [ai](), [ai-agent](), [data](), [data-science](), [datasets](), [engineering](), [llms](), [quality](), [science](), [trust]() ### AI overview Airbnb describes an agent harness for data science that embeds scientific methodology around an AI model. The system guides agents through framing questions, selecting evidence, and recording decisions so unstructured-data investigations can be reproduced, audited, challenged, and extended across languages, geographies, and LLM-based products. ### Source excerpt How Airbnb's agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By: Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you'll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can't see is the investigation that produced them: the methods it chose, the evidence it weighed, how much to trust it, or whether a second request would agree. All that reaches you is the polish. The model is undeniably intelligent, but intelligence without methodology is not science. LLMs certainly make for confident scientists, but we need them to be responsible ones. Smarter models help, but intelligence has never been the whole of science, in people or in machines. The method is as much the product as the answer. That is the idea behind the agent harness we built for data science: the methodology itself, built as infrastructure around the model. It governs how an AI agent operates, from framing a question to selecting evidence to recording decisions, so results can be reproduced, audited, and challenged, and the method shared, inspected, and built on. The challenge of unstructured data exploration In 2025, Airbnb was preparing to launch an AI customer service assistant. Before it could ship, we needed to understand exactly what kinds of situations it would face in the real world. That included rare events that could be risky for AI to interact with, and involved examining their taxonomy and prevalence to create the datasets that would help us build a more responsible product. The investigative work to do this was rigorous, but the process was deeply artisanal. Months of high-touch iteration went into each investigation, from finding the right data, reviewing samples with experts, and generating representative datasets, and the method was manually curated across notebooks, tables, docs, and indiv ## How LLMs Handle Memory Through Context and Surrounding Applications DevFeed: [How LLMs Handle Memory Through Context and Surrounding Applications]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-15T15:31:12Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Large Language Model](), [context window](), [App](), [long-context]() Tags: [article](), [context](), [context-window](), [cost](), [latency](), [llms](), [memory]() ### AI overview LLMs do not usually retain personal or persistent memory between interactions. Surrounding applications create the appearance of memory by storing messages, maintaining summaries, retrieving relevant information, and supplying it to the model. As conversations grow, this processing increases cost and latency, while context-window limits require older information to be removed, summarized, or stored elsewhere. ### Source excerpt In this article, we will learn how LLMs handle memory so that they are useful to end users in performing complex tasks that require conversation and holding context. ## 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. ## From Autocomplete to Intelligence: How Language Became the Operating System of AI DevFeed: [From Autocomplete to Intelligence: How Language Became the Operating System of AI]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-09-15T10:17:07Z Content type: opinion Language: en Sources: [Wix Engineering]() Topics: [Artificial Intelligence](), [Language models](), [Large Language Model](), [ChatGPT](), [OpenAI]() Tags: [ai](), [article](), [chatgpt](), [language-models](), [large-language-models-llms](), [openai]() ### AI overview This article examines why language is closely tied to intelligence in both humans and artificial intelligence. It argues that AI became significantly more powerful by learning language exceptionally well, then introduces how large language models acquire intelligence through models that represent the world and reduce uncertainty. ### Source excerpt Introduction Every week brings a new AI model, a new coding assistant, a new agent framework. The pace can feel overwhelming. Yet beneath the churn lies a single, surprisingly simple fact - the fact this whole revolution turns on: AI became so powerful because it learned language exceptionally well. In this article I want to step away from the weekly headlines and ask a more basic question: why is language so tightly bound to intelligence - human intelligence in general, and artificial... ## How Everpure proposes reducing GPU idle time by improving AI data access DevFeed: [How Everpure proposes reducing GPU idle time by improving AI data access]() Original publisher: [Read original article]() Author: Chris Mellor Published: 2026-09-15T08:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Infrastructure](), [GPU](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [AI Agent](), [data-processing](), [data]() Tags: [agents](), [ai](), [ai-infrastructure](), [ai-ml](), [data-processing](), [gpu](), [hardware](), [rag](), [sponsored]() ### AI overview This sponsored feature describes Everpure's approach to reducing GPU idle time in AI systems by improving access to large-scale insurance data. It discusses central metadata indexing, storage performance, self-describing data, and integration with Nvidia GPU infrastructure for AI agents and retrieval-augmented generation. ### Source excerpt SPONSORED FEATURE: The vendor's AI solutions are dedicated to increasing GPU utilization and avoiding costly GPUs doing nothing while waiting for data ## 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 [Next page]()