# Hallucinations Published articles for Hallucinations. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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 ## ChatGPT-Using Lawyer Cited Its Fake Witnesses and Police Testimony in Court DevFeed: [ChatGPT-Using Lawyer Cited Its Fake Witnesses and Police Testimony in Court]() Original publisher: [Read original article]() Author: EditorDavid Published: 2026-09-15T05:34:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [Artificial Intelligence](), [ChatGPT](), [OpenAI]() Tags: [ai](), [ai-hallucinations](), [chatgpt](), [hallucinations](), [news](), [openai](), [verify]() ### AI overview A New Mexico defense lawyer was fined and held in contempt after submitting a court brief containing fabricated witnesses and police testimony generated by ChatGPT. The case highlights the risks of relying on AI-generated summaries without verifying their accuracy. ### Source excerpt Reuters reports: A defense lawyer appealing his client's murder conviction submitted a brief containing made-up police testimony and witnesses fabricated by OpenAI's ChatGPT, New Mexico's highest court said. The New Mexico Supreme Court on Wednesday fined the attorney, Stephen Aarons [for $5,000], and held him in contempt for failing to verify the accuracy of the court filing, which Aarons said he prepared with help from the AI program... and said they will refer him to an attorney disciplinary board for investigation. Aarons in a statement to Reuters said he had used ChatGPT to summarize the trial proceedings when he agreed to take up the defendant's appeal last year, and did not understand the degree to which AI could "hallucinate" facts. "I am remorseful but hopeful that the disciplinary board takes into account it was an honest mistake," he said.... Dozens of lawyers have been sanctioned for filing briefs where AI made up case citations or misquoted the law. Aarons' filing appears to have gone further, containing fabricated witness testimony in a criminal appeal... Aarons told the court at an August 21 hearing that he fed a computer-generated transcript and other case materials to ChatGPT, presuming it would generate "a bulletproof summary." The justices sounded incredulous that Aarons was not fully aware of how AI can make mistakes. "Counsel, do you watch the news? Do you listen to the radio? Do you read anything about what's going on in the world?" Justice C. Shannon Bacon said at the hearing. "Because the problem with lawyers relying on AI hallucinations is an above-the-fold story every single day." "OpenAI did not immediately respond to a request for comment." Read more of this story at Slashdot. ## Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence DevFeed: [Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence]() Original publisher: [Read original article]() Published: 2026-07-30T20:36:36Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Google](), [AI-generated research reports](), [Large language models (LLMs)](), [Hallucination detection](), [LLM evaluation / benchmarking]() Tags: [agents](), [ai](), [autonomous-agents](), [benchmarks](), [evaluation](), [general-science](), [google](), [hallucinations](), [language-models](), [machine-intelligence](), [natural-language-processing](), [research](), [research-prototype]() ### AI overview Google Research introduces the Science One Framework, an experimental autonomous research prototype built around Chain-of-Evidence. It is designed to make AI-generated research verifiable by linking claims to supporting evidence and by auditing papers against their code and evidence. The article reports that the framework eliminates phantom references and produces fully verifiable scores in the described evaluations. ### Source excerpt General Science ## No, People Don't Want More AI In Their Life DevFeed: [No, People Don't Want More AI In Their Life]() Original publisher: [Read original article]() Author: hello@smashingmagazine.com (Vitaly Friedman) Published: 2026-07-15T10:00:00Z Content type: opinion Language: en Sources: [Articles on Smashing Magazine -- For Web Designers And Developers]() Topics: [Artificial Intelligence](), [User experience (UX)](), [systems]() Tags: [ai](), [data-quality](), [design](), [design-patterns](), [hallucinations](), [systems](), [ux]() ### AI overview The article argues that people generally do not want more AI when it arrives as disconnected features, separate tools, or imposed workflows. It links low adoption and retention to high delivery costs, reputation risks, fragmented systems, technical debt, poor data quality, and the effort required to check and correct AI-generated output. It also describes resistance and anxiety connected to fears that AI may replace work. ### Source excerpt Many companies assume everyone craves new AI features. But the reality is that most people don't want more AI -- at least not in the way most AI leaders envision it. Brought to you by Design Patterns For AI Interfaces, **friendly video courses on UX** and design patterns by Vitaly. ## Sampling args in llama-server DevFeed: [Sampling args in llama-server]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-07-01T18:28:35Z Content type: tutorial Language: en Sources: [Alex Ewerlof Notes]() Topics: [llama.cpp](), [Large Language Model](), [Inference](), [quantization](), [Benchmark]() Tags: [benchmark](), [hallucinations](), [inference](), [llama-cpp](), [llm](), [quantization]() ### AI overview A reference guide to llama.cpp sampling and repetition parameters, covering common failure modes in local and quantized language models, parameter ranges and defaults, workload-specific tuning, modern sampling methods, and ways to accelerate experimentation. ### Source excerpt Reducing repetition, hallucinations, degradation, while making inference faster! ## Token efficiency: getting more signal into the context window DevFeed: [Token efficiency: getting more signal into the context window]() Original publisher: [Read original article]() Author: Jim Allen Wallace Published: 2026-07-01T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [long-context](), [Transformer](), [Large Language Model](), [Retrieval Augmented Generation (RAG)]() Tags: [agentic](), [context-window](), [efficiency](), [generation](), [hallucinations](), [llms](), [rag](), [reasoning](), [tech-de](), [tokens](), [transformers]() ### AI overview This guide explains why adding more context can worsen model performance. It examines token efficiency, the "lost in the middle" effect, context rot, attention limits in transformers, and the gap between advertised and effective context windows, with implications for RAG and agentic systems. ### Source excerpt You've probably hit this counterintuitive moment: you give your model more context to work with, expecting better answers, and the answers get worse. More tokens were supposed to mean more information, more grounding, fewer hallucinations. Instead, yo... ## Doximity Ask DevFeed: [Doximity Ask]() Original publisher: [Read original article]() Author: Doximity Published: 2026-06-23T09:30:00Z Content type: article Language: en Sources: [Doximity]() Topics: [Artificial Intelligence](), [Retrieval-Augmented Generation](), [Hallucination detection]() Tags: [agentic](), [ai](), [grounding](), [hallucinations](), [retrieval-augmented-generation](), [review](), [verification]() ### AI overview The article explains how Doximity Ask approaches reliability in clinical AI. It describes a HIPAA-compliant clinical reference platform that combines retrieval-augmented generation, validated tools, a reasoning model, citation-grounded responses, automated verification, physician-authored evaluation, and production monitoring. It argues that reliability is a system property spanning retrieval, ranking, reasoning, tool use, verification, and evaluation. ### Source excerpt Medical AI has attracted confident claims: perfect scores, no hallucinations, a system that gracefully declines whenever the evidence runs out. But are these dimensions the right way to think about a tool that physicians use to make real-world decisions? For clinical AI, reliability is not just a question of whether an answer is correct. It is also a question of whether a physician can understand where the answer came from, evaluate the supporting evidence, and identify situations where uncertainty remains. How Doximity Ask Answers Clinical Questions Few domains face stakes as high as medical AI, where system performance can directly influence patient outcomes, clinical workflows, and healthcare professionals' decisions. Clinical reference requires traceable grounding, numerically exact doses and scores, and explicit handling of situations in which the available evidence is insufficient to support an answer. At the same time, healthcare providers often work under significant time constraints and have exceptionally high expectations for the performance and reliability of AI systems. Doximity Ask is a HIPAA compliant composite clinical reference platform for question answering at the point of care. It combines retrieval-augmented generation over a physician-governed evidence library, multi-turn agentic tool use through a fixed registry of validated calculators and structured lookups, and a post-trained reasoning model that orchestrates retrieval, tool calls, and citation-grounded generation. Automated verifiers, physician-authored rubrics, and in-product physician review form one evaluation loop that grades releases, defines training rewards, and monitors production. Reliability Is A System Property Over 150 health systems have purchased the Doximity Clinical AI Suite including 8 of the top 20 hospitals in the country, therefore reliability is paramount. The reliability of a clinical AI system depends on more than the capabilities of its underlying language model. Pro ## Real-world grounding in agentic AI DevFeed: [Real-world grounding in agentic AI]() Original publisher: [Read original article]() Author: Rose Yu Published: 2026-06-08T19:00:00Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [AI Bots](), [dashboards]() Tags: [agentic-ai](), [agentic-ai-grounding](), [ai](), [ai-agent-hallucinations](), [ai-agents](), [calibrated-uncertainty](), [formal-verification-ai](), [foundation-models-physical-ai](), [hallucinations](), [large-language-models-llms](), [machine-learning](), [physical-ai](), [physics-guided-deep-learning](), [robotics](), [robots](), [uncertainty-quantification-llms]() ### AI overview The article proposes four approaches for grounding AI agents in physical-world operational environments. It argues that integrating domain data, physical principles, and simulations can reduce harmful hallucinations and improve safe, trustworthy agent behavior. ### Source excerpt Four approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments. ## Ground truth is a process, not a dataset DevFeed: [Ground truth is a process, not a dataset]() Original publisher: [Read original article]() Author: Venkatesh Saligrama Published: 2026-06-03T15:56:57Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [ai observability](), [AI Chat]() Tags: [ai](), [ai-fact-checking](), [ai-generated-research-reports](), [audit-then-score](), [benchmarking](), [conversational-ai](), [datasets](), [deep-research-verification](), [deepfact-bench](), [evaluation](), [fact-verification](), [fact-verification-benchmark](), [generative-ai](), [ground-truth-benchmark-quality](), [hallucination-detection](), [hallucinations](), [human-ai-evaluation](), [large-language-models-llms](), [llm-evaluation-benchmarking](), [long-context](), [machine-learning](), [responsible-ai](), [retrieval-augmented-generation-rag]() ### AI overview The article argues that evaluating factuality in long AI-generated research reports requires a process-based approach to ground truth. It introduces audit-then-score and accompanying datasets for benchmarking AI fact checkers. ### Source excerpt Automatically fact-checking long, AI-generated research reports poses new challenges -- including benchmarking. ## What to consider before asking an AI chatbot for health advice DevFeed: [What to consider before asking an AI chatbot for health advice]() Original publisher: [Read original article]() Author: Phil Muncaster Published: 2026-05-27T08:50:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Artificial Intelligence](), [Chat Bot](), [Generative AI](), [online privacy](), [Security](), [Large Language Model](), [ChatGPT](), [Microsoft](), [amazon]() Tags: [ai](), [amazon](), [chatgpt](), [data](), [genai](), [generative-ai](), [hallucinations](), [llm](), [llms](), [microsoft](), [privacy](), [security]() ### AI overview This article examines the risks of using AI chatbots for health advice, including hallucinations, inconsistent answers, incorrect guidance, and exposure of sensitive personal information. It explains why people turn to chatbots for medical questions and why these tools should not replace physicians. ### Source excerpt Using chatbots for medical advice could elicit hallucinations and even expose you to security and privacy risks. Here's what's at stake and how to stay safe. ## Bringing the real world to your AI application using Firebase AI Logic DevFeed: [Bringing the real world to your AI application using Firebase AI Logic]() Original publisher: [Read original article]() Author: Ankita Saxena; Ken Nevarez Published: 2026-05-19T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [Google Maps](), [Generative AI](), [Large Language Model](), [JavaScript](), [Web Components](), [API](), [Mobile]() Tags: [ai](), [ai-logic](), [api](), [firebase](), [firebase-ai-logic](), [gemini](), [generative-ai](), [google-maps](), [hallucinations](), [javascript](), [tutorials](), [web]() ### AI overview Firebase introduces Grounding with Google Maps in the Firebase AI Logic SDK to help mobile and web applications produce location-aware responses based on real-world geospatial data. The article explains configuring the model, plotting places with Google Maps Web Components, and using grounding metadata for citations and coordinates. ### Source excerpt News, tutorials, and updates from the Firebase team. ## GPT-5.5 Instant: smarter, clearer, and more personalized DevFeed: [GPT-5.5 Instant: smarter, clearer, and more personalized]() Original publisher: [Read original article]() Published: 2026-05-05T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [personalization]() Tags: [chatgpt](), [hallucinations](), [model](), [personalization](), [product](), [search](), [updates]() ### AI overview GPT-5.5 Instant updates ChatGPT with smarter, clearer, and more personalized responses. The article highlights improved factuality, fewer hallucinated claims, stronger performance across visual reasoning, math, and science, better image analysis, and more effective use of context from past chats, files, and connected Gmail. ### Source excerpt GPT-5.5 Instant updates ChatGPT's default model with smarter, more accurate answers, reduced hallucinations, and improved personalization controls. ## Proofs and Essays Are Paths: An LLM ↔ Prover Loop for Falsifying Hallucinations DevFeed: [Proofs and Essays Are Paths: An LLM ↔ Prover Loop for Falsifying Hallucinations]() Original publisher: [Read original article]() Published: 2026-04-30T00:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [Large Language Model](), [Automated reasoning](), [Graphs](), [Artificial Intelligence](), [Math and Logic]() Tags: [grounding](), [hallucinations](), [knowledge-graph](), [llm](), [reasoning](), [smt](), [verification](), [verify]() ### AI overview This essay proposes a bidirectional loop between a large language model and a theorem prover. LLM outputs become propositions and graph connections that the prover checks, while verification results feed back into the model. In this framework, hallucinations appear as knowledge-graph paths with edges that fail formal verification. ### Source excerpt If a proof is a path through a formal space and an essay is a path through a semantic one, can we close the loop -- translate LLM outputs into a theorem prover, build a knowledge graph of verifiable propositions, and use the gaps as a signal for hallucination? Notebook ideas from March 2024 expanding on the SMT-grounding piece. ## Worth Reading: Shameless Guesses, Not Hallucinations DevFeed: [Worth Reading: Shameless Guesses, Not Hallucinations]() Original publisher: [Read original article]() Published: 2026-04-07T05:36:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [Artificial Intelligence]() Tags: [ai](), [hallucinations](), [worth-reading]() ### AI overview The article discusses Scott Alexander's view that AI errors are better understood as shameless guesses than hallucinations, because training rewards correct answers without sufficiently penalizing incorrect ones. It also comments on the business incentives behind AI systems' reluctance to admit uncertainty and the use of "hallucinations" as a label. ### Source excerpt In a recent article, Scott Alexander made an interesting point: What AI produces are not hallucinations but shameless guesses (also known as bullshit) because the training process rewards the correct answers but does not penalize the incorrect ones. After all, having an AI model say, "I don't know that" is not good for business, is it? On a tangential note, calling those blunders hallucinations was a marketing masterstroke. Not being a native English speaker, I might be missing some nuances, but I feel like hallucinations might be something you're not responsible for (some of the time), whereas we all know who's responsible for bullshit and shameless guesses - and responsibility is something the AI companies are clearly trying to stay as far away from as possible. Read more ... ## Ask AIMee: An accessible accessibility-focused AI chatbot DevFeed: [Ask AIMee: An accessible accessibility-focused AI chatbot]() Original publisher: [Read original article]() Author: Jared Smith Published: 2026-03-31T16:49:07Z Content type: release Language: en Sources: [WebAIM Blog]() Topics: [Accessibility](), [Artificial Intelligence](), [Chat Bot](), [Large Language Model](), [qwen](), [Code]() Tags: [accessibility](), [ai](), [chat](), [chatbots](), [hallucinations](), [llm](), [qwen](), [uncategorized]() ### AI overview WebAIM introduces AIMee, an AI-powered conversational chatbot focused on accessibility. It is designed to support users with disabilities by answering accessibility questions, reviewing code, drafting policies, explaining concepts, and generating resources, checklists, and quizzes. AIMee primarily uses the Qwen 3 Coder large language model with additional safeguards, but its answers can still contain hallucinations and should be verified. ### Source excerpt We're happy to introduce AIMee - an easy-to-use, AI-powered conversational chatbot focused on accessibility. AIMee has been designed to be highly accessible to users with disabilities. Ask her accessibility questions to get quick answers and guidance. The name "AIMee" plays off of the "AIM" (Accessibility In Mind) from "WebAIM" and also "AI". Here are some [...] ## IBM and UC Berkeley Diagnose Why Enterprise Agents Fail Using IT-Bench and MAST DevFeed: [IBM and UC Berkeley Diagnose Why Enterprise Agents Fail Using IT-Bench and MAST]() Original publisher: [Read original article]() Author: Ayhan Sebin; Rohan Arora; Saurabh Jha Published: 2026-02-18T16:15:45Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Benchmark](), [ibm](), [Large Language Model](), [site-reliability-engineering](), [incident](), [Kubernetes](), [finops](), [Security]() Tags: [agentic](), [agents](), [analysis](), [automation](), [benchmarks](), [enterprise](), [evaluation](), [gpt-oss](), [hallucinations](), [ibm](), [incident](), [kubernetes](), [llm](), [logs](), [loops](), [metrics](), [performance](), [research](), [systems](), [tool]() ### AI overview IBM Research and UC Berkeley analyze why agentic LLM systems fail in enterprise IT automation using ITBench traces and the MAST failure taxonomy. Their analysis of 310 SRE traces compares Gemini-3-Flash, Kimi-K2, and GPT-OSS-120B, identifying verification errors, cascading failures, premature termination, looping, and hallucinations as major reliability problems. ### Source excerpt IBM Research and UC Berkeley collaborated to study how agentic LLM systems break in real-world IT automation, for tasks involving incident triage, logs/metrics queries, and Kubernetes actions in long-horizon tool loops. Benchmarks typically reduce performance to a single number, telling you whether an agent failed but never why. To solve this black-box problem, we applied MAST (Multi-Agent System Failure Taxonomy), an emerging practice for diagnosing agentic reliability ). ## Functional PoCs in less than a minute? Julen Garrido Estévez puts Burp AI to the test DevFeed: [Functional PoCs in less than a minute? Julen Garrido Estévez puts Burp AI to the test]() Original publisher: [Read original article]() Author: Hassan Ud-Deen Published: 2026-01-16T00:00:00Z Content type: article Language: en Sources: [PortSwigger Blog]() Topics: [Artificial Intelligence](), [Prompt Engineering](), [Security](), [Vulnerabilities](), [Web]() Tags: [ai](), [analysis](), [cost](), [generation](), [hallucinations](), [insights](), [security](), [time](), [vulnerabilities](), [web]() ### AI overview A pentester evaluates Burp AI in Repeater using controlled tests across Web Security Academy labs, a deliberately vulnerable shop, and personal environments. The article compares prompting styles by cost, requests, time to proof of concept, adherence, and hallucinations, finding that clear free-form prompts provided the best overall balance and produced a valid PoC at low cost. ### Source excerpt Note: This is a guest post by pentester Julen Garrido Estévez (@b3xal). Methodology Key results Examples Key learnings Prompt template A pentester's POV on Burp AI Pentester Julen Garrido Es ## Gemini-backed Paper Assistant Tool provides automated feedback for theoretical computer scientists at STOC 2026 DevFeed: [Gemini-backed Paper Assistant Tool provides automated feedback for theoretical computer scientists at STOC 2026]() Original publisher: [Read original article]() Published: 2025-12-15T17:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Algorithms](), [Algorithms & Theory](), [Google](), [Inference](), [Chain-of-thought]() Tags: [ai](), [algorithms-theory](), [chain-of-thought](), [evaluation](), [experimental](), [gemini](), [generative-ai](), [google](), [hallucinations](), [inference](), [natural-language-processing](), [research](), [technical](), [verify]() ### AI overview Google Research describes Paper Assistant Tool (PAT), an experimental Gemini-powered system tested for STOC 2026 that gives theoretical computer science authors automated pre-submission feedback. PAT uses inference scaling and multiple reasoning and evaluation traces to identify calculation errors, logic errors, inconsistencies, and other issues in papers. ### Source excerpt Algorithms & Theory ## Benchmarking LLMs in Real-World Applications: Pitfalls and Surprises DevFeed: [Benchmarking LLMs in Real-World Applications: Pitfalls and Surprises]() Original publisher: [Read original article]() Author: Jean Alves Published: 2025-11-25T15:31:00Z Content type: article Language: en Sources: [Feedzai]() Topics: [LLM evaluation / benchmarking](), [benchmarking](), [Generative AI](), [Security, Privacy and Abuse Prevention]() Tags: [benchmarking](), [computer-vision](), [evaluation](), [financial-fraud](), [fraud-prevention](), [hallucinations](), [large-language-models](), [llms](), [machine-learning](), [trust]() ### AI overview This article explains how Feedzai's ScamAlert uses Generative AI to identify interpretable scam signals from submitted screenshots, and why systematic evaluation and benchmarking are needed to assess consistency, explanations, performance, and limitations such as hallucinations. ### Source excerpt By Jean V. Alves and Ferran Pla Fernández Moving beyond binary classification provides novel insights. In the real world, scams rarely present themselves in black and white. Fraudsters exploit nuance, impersonate legitimate brands, and mask malicious intent with seemingly ordinary behavior. That's why Feedzai has launched ScamAlert (patent pending), a Generative AI-based system innovating on the current paradigm of scam prevention, in response to this growing challenge. Traditional detection systems treat the problem as a binary choice: scam or not a scam, often outputting an estimated "scam likelihood" measure. This value, even if accurate, doesn't tell users why something is risky or what they should watch out for, leaving them with little guidance on how to stay safe. A potential scam SMS The binary approach can often suffer from a lack of context. While a text message may look suspicious in a vacuum (e.g., a payment request via a less safe method) the user may have other reasons to believe in its legitimacy, such as a past history of such requests. Consequently, an incorrect risk estimate based on missing context may lead users to distrust the system's abilities. A traditional binary classification system outputs only a risk estimate ScamAlert, on the other hand, makes judgements on what it knows. Users submit a screenshot of the suspected scam, and ScamAlert identifies observable red flags, patterns or behaviors that are often associated with fraud, such as suspicious links or spelling errors. This approach empowers the user with interpretable insights into the detected risk signals, instead of a vague numeric value. This places the user in the driver's seat, by presenting them with the facts and enhancing their awareness and judgment. To fully understand a systems' ability to perform this task, we pair this labelling approach with a rigorous evaluation and benchmarking protocol. We test the consistency of model outputs for the same input; the model's ability t ## Building Deep Research: How we Achieved State of the Art DevFeed: [Building Deep Research: How we Achieved State of the Art]() Original publisher: [Read original article]() Author: Michael Griff; Dean Sacoransky; Noah Nefsky Published: 2025-11-24T17:40:14Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI Bots](), [AI Chat](), [AI-generated research reports]() Tags: [agent](), [agents](), [architecture](), [building](), [compression](), [engineering](), [hallucinations](), [latency](), [llms](), [optimization](), [orchestration](), [research](), [search](), [tools](), [workflows]() ### AI overview The article outlines principles for building deep-research agent harnesses that remain effective as models improve. It emphasizes simplified orchestration, reliable tool calling, and context engineering to reduce context overload, hallucinations, and latency. ### Source excerpt The task of building an agent harness is to create a software layer that enhances a model's runtime execution through context management, tool invocations, loop control, orchestration, and error handling. Building applications on top of rapidly improving models is, however, a modern engineering challenge. How can we design software today that absorbs the performance gains from future model releases? ## Claude Code's Context Access and Terminal Workflow DevFeed: [Claude Code's Context Access and Terminal Workflow]() Original publisher: [Read original article]() Author: Jack Rosa Published: 2025-11-18T14:00:00Z Content type: opinion Language: en Sources: [Hashrocket]() Topics: [Claude Code](), [Claude](), [Terminal](), [Bash]() Tags: [ai](), [bash](), [claude](), [claude-code](), [context](), [hallucinations](), [permission](), [rails](), [ruby](), [terminal](), [workflow]() ### AI overview An opinion article assessing Claude Code's codebase context access, permission workflow, data-handling considerations, and terminal-based verification capabilities. It advises developers to review proposed actions critically because the tool can hallucinate. ### Source excerpt Claude code is a powerful AI toolset that runs right in your terminal. While providing a lot of impressive utility, it also suffers from the issues that arise from similar AI toolings with the addition of an expensive pricing model. Context is Important To me, the main selling point for Claude Code is its ability to read through your entire codebase; a big shortcoming of many AI workflows is the model only partially understanding an issue due to it not having enough of the project's context and convention to be effective. Claude Code has the ability to access all of the files in the directory where you initiated the session, and can even ask for permission to search through extraneous directories. While potentially helpful, this is also a little sketchy when considering that everything Claude is processing is getting sent over the wire to Anthropic's servers; according to their privacy policy, the data sent over for processing will not be used for LLM training unless the user specifically opts in or the content has been flagged for a Trust & Safety Review. It's also important to be aware that Claude's data retention policy has changed in recent months and will likely continue to change. Step by Step When you start a Claude Code session and give it a task to complete, it will usually try to break down the task into steps to complete. Upon starting a 'step', Claude will show you what it wants to do, and ask you for permission to do it. In my experience, I always had three options when presented with Claude's suggestion: accept the suggested action, always accept the suggested action, or do not accept and tell Claude to do something different. I would urge any developer to stick to the first and last option. Take the time to look at Claude's suggestion critically and decide if it seems like the right way to go. Claude is usually logical, but also susceptable to hallucinations. A big advantage of Claude Code living in the terminal, is that it has the ability to run bash ## How we made our internal data warehouse AI-first DevFeed: [How we made our internal data warehouse AI-first]() Original publisher: [Read original article]() Author: Dmitry Pavlov Published: 2025-11-12T16:16:15Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI Chat]() Tags: [ai](), [analytics](), [clickhouse](), [data](), [data-infrastructure](), [hallucinations](), [llms](), [sql](), [workflow]() ### AI overview ClickHouse describes moving its internal data warehouse from a BI-first workflow to an AI-first analytics platform so users can ask questions and receive insights without writing SQL. ### Source excerpt Learn how we evolved our internal 2.1 PB data warehouse from traditional BI to an AI-first analytics platform ## Consensus accelerates research with GPT-5 and Responses API DevFeed: [Consensus accelerates research with GPT-5 and Responses API]() Original publisher: [Read original article]() Published: 2025-10-23T09:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [API](), [OpenAI](), [Graphs](), [Library]() Tags: [agents](), [analysis](), [api](), [discovery](), [gpt](), [graph](), [hallucinations](), [library](), [multi-agent](), [openai](), [research](), [responses](), [search](), [workflow]() ### AI overview Consensus built Scholar Agent, a multi-agent research assistant powered by GPT-5 and the Responses API. The system plans research questions, searches scientific papers and citation data, reads and interprets papers, and synthesizes cited findings. Its narrow agent roles are intended to improve precision and reduce hallucinations, shortening research tasks from weeks to minutes. ### Source excerpt Consensus uses GPT-5 and OpenAI's Responses API to power a multi-agent research assistant that reads, analyzes, and synthesizes evidence in minutes--helping over 8 million researchers accelerate scientific discovery. ## Build Better Agents With MorphLLM DevFeed: [Build Better Agents With MorphLLM]() Original publisher: [Read original article]() Published: 2025-08-25T00:00:00Z Content type: article Language: en Sources: [The Fly Blog]() Topics: [AI Agent](), [Code](), [developer velocity](), [Tooling]() Tags: [agents](), [ai-agent](), [cdn](), [close-to-users](), [code](), [cost](), [deploy-app-servers](), [developer-velocity](), [docker](), [elixir](), [fly](), [fly-io](), [hallucinations](), [heroku-alternative](), [heroku-competitor](), [hosting](), [i](), [networking](), [postgresql-clusters](), [servers](), [tooling]() ### AI overview The article presents MorphLLM's Morph Fast Apply as a semantic, structure-aware code-editing tool for AI agents. It addresses the inefficiency and unreliability of full-file rewrites by enabling precise, context-aware edits that reduce token use, compute, cost, and time. ### Source excerpt I'm an audiophile, which is a nice way to describe someone who spends their children's college fund on equipment that yields no audible improvement in sound quality. As such, I refused to use wireless headphones for the longest time. The fun thing about wired headphones is when you forget they're on and you stand up, you simultaneously cause irreparable neck injuries and extensive property damage. This eventually prompted me to buy good wireless headphones and, you know what, I break fewer things now. I can also stand up from my desk and not be exposed to the aural horrors of the real world. This is all to say, sometimes you don't know how big a problem is until you solve it. This week, I chatted to the fine people building MorphLLM, which is exactly that kind of solution for AI agent builders. Slow, Wasteful and Expensive AI Code Changes If you're building AI agents that write or edit code, you're probably accepting the following as "the way it is": Your agent needs to correct a single line of code, but rewrites an entire file to do it. Search-and-replace right? It's fragile, breaks formatting, silently fails, or straight up leaves important functions out. The result is slow, inaccurate code changes, excessive token use, and an agent feels incompetent and unreliable. Full file rewrites are context-blind and prone to hallucinations, especially when editing that 3000+ line file that you've been meaning to refactor. And every failure and iteration is wasted compute, wasted money and worst of all, wasted time. Why We Aren't Thinking About This (or why I wasn't) AI workflows are still new to everyone. Best practices are still just opinions and most tooling is focused on model quality, not developer velocity or cost. This is a big part of why we feel that slow, wasteful code edits are just the price of admission for AI-powered development. In reality, these inefficiencies become a real bottleneck for coding agent tools. The hidden tax on every code edit adds up and your [Next page]()