<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>StackSweep</title><description>Tech news, distilled from the signal.</description><link>https://stacksweep.dev/</link><item><title>Five Hypotheses for Why LLMs Fail at Tabular Data.</title><link>https://stacksweep.dev/llm-tabular-prediction-dimensionality-collapse/</link><guid isPermaLink="true">https://stacksweep.dev/llm-tabular-prediction-dimensionality-collapse/</guid><description>A systematic study rules out four plausible explanations for why LLMs underperform classical ML on tabular classification, and finds the real culprit: accuracy degrades with feature count in a way no noise-corrupted classical model reproduces, and the model&apos;s own explanations don&apos;t match what it computed.</description><pubDate>Wed, 05 Aug 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>llms</category><category>in-context-learning</category><category>tabular-data</category></item><item><title>What Actually Keeps an AI Benchmark Useful? Scale</title><link>https://stacksweep.dev/ai-benchmark-saturation-study/</link><guid isPermaLink="true">https://stacksweep.dev/ai-benchmark-saturation-study/</guid><description>A systematic study of 60 LLM benchmarks finds 29 have saturated: top models are statistically indistinguishable. Age and test set size predict saturation; private test sets, multilingual scope, and open-ended formats don&apos;t protect against it once age is controlled for.</description><pubDate>Tue, 04 Aug 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>benchmarks</category><category>evaluation</category><category>llms</category></item><item><title>How GPT-Live Kills the Turn Detector: A System Design Teardown</title><link>https://stacksweep.dev/gpt-live-full-duplex-voice-system-design/</link><guid isPermaLink="true">https://stacksweep.dev/gpt-live-full-duplex-voice-system-design/</guid><description>OpenAI&apos;s GPT-Live writeup is a real systems engineering case study: full-duplex audio, hot model handoffs, a 6-to-1 round-trip protocol, and capacity planning that isn&apos;t about GPU throughput. We break down the five design patterns worth studying, with a quiz.</description><pubDate>Tue, 04 Aug 2026 00:00:00 GMT</pubDate><category>infra</category><category>system-design</category><category>voice-ai</category><category>infra</category><category>openai</category></item><item><title>The Hidden Tax of Messy Code: More Tokens, More Backtracking, Same Result</title><link>https://stacksweep.dev/messy-code-hidden-tax-coding-agents/</link><guid isPermaLink="true">https://stacksweep.dev/messy-code-hidden-tax-coding-agents/</guid><description>A SonarSource controlled study finds code cleanliness doesn&apos;t change whether coding agents pass a task, but messy code makes them burn ~8% more tokens and re-open edited files 34% more often. The quality tax is efficiency, not success.</description><pubDate>Tue, 07 Jul 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>agents</category><category>code-quality</category><category>llms</category></item><item><title>Same Weights, Better Agent: Teaching Models to Tune Their Own Harness</title><link>https://stacksweep.dev/self-harness-self-tuning-agents/</link><guid isPermaLink="true">https://stacksweep.dev/self-harness-self-tuning-agents/</guid><description>Self-Harness lets an agent improve its own scaffolding, no stronger model or human engineer required. By mining its failure traces and regression-testing edits, MiniMax M2.5 jumped from 40.5% to 61.9% on Terminal-Bench-2.0 with frozen weights.</description><pubDate>Tue, 23 Jun 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>agents</category><category>llms</category><category>harness</category></item><item><title>Generate, Critique, Repair: The RL Loop Behind a Gold-Medal Proof Model</title><link>https://stacksweep.dev/maxproof-generate-critique-repair/</link><guid isPermaLink="true">https://stacksweep.dev/maxproof-generate-critique-repair/</guid><description>MiniMax&apos;s MaxProof clears the IMO gold-medal threshold by wrapping one model in a generate-critique-repair loop. Test-time search adds 8 to 10 points over one-shot, but the conservative verifier is what makes it work.</description><pubDate>Sun, 14 Jun 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>llms</category><category>reasoning</category><category>reinforcement-learning</category></item><item><title>Why You Can&apos;t Fix Every LLM Error, But Can Fix the Ones That Matter</title><link>https://stacksweep.dev/patch-local-llm-reliability/</link><guid isPermaLink="true">https://stacksweep.dev/patch-local-llm-reliability/</guid><description>A new paper proves universal LLM reliability is impossible with finite interventions, but reliability inside a bounded deployment is tractable. Failure modes grow only logarithmically, so a domain-specific library of tens of interventions can cover them.</description><pubDate>Fri, 12 Jun 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>llms</category><category>reliability</category><category>agents</category></item><item><title>word2vec, But for Food: Ingredient Embeddings You Can Do Math On</title><link>https://stacksweep.dev/food-ingredient-embeddings-epicure/</link><guid isPermaLink="true">https://stacksweep.dev/food-ingredient-embeddings-epicure/</guid><description>Epicure trains word2vec-style embeddings on 4.1M recipes, turning cuisine, nutrients, and taste into linear directions you can navigate with vector arithmetic, with a tunable chemistry-vs-recipe-context knob.</description><pubDate>Wed, 10 Jun 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>embeddings</category><category>llms</category></item><item><title>Where the Tokens Go: 59% of Agentic Coding Cost Is Code Review</title><link>https://stacksweep.dev/tokenomics-agentic-software-engineering/</link><guid isPermaLink="true">https://stacksweep.dev/tokenomics-agentic-software-engineering/</guid><description>A Concordia study traces token spend across a multi-agent coding system and finds 59.4% of it goes to code review, not writing code. Input tokens are the hidden tax: more than half of all consumption is the model re-reading context.</description><pubDate>Wed, 10 Jun 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>agents</category><category>llms</category><category>cost</category></item><item><title>Is Grep All You Need? The Harness Matters More Than the Search</title><link>https://stacksweep.dev/grep-vs-vector-agentic-search/</link><guid isPermaLink="true">https://stacksweep.dev/grep-vs-vector-agentic-search/</guid><description>A PwC study finds plain lexical grep beats vector search for LLM agents on long-memory QA, with gaps up to 23 points. But the agent harness matters as much as the retrieval method: the same model swings 16 points across harnesses.</description><pubDate>Tue, 09 Jun 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>agents</category><category>retrieval</category><category>llms</category></item><item><title>Coding Agents Collapse as Backend Rules Stack Up</title><link>https://stacksweep.dev/constraint-decay-llm-backend-generation/</link><guid isPermaLink="true">https://stacksweep.dev/constraint-decay-llm-backend-generation/</guid><description>A new study finds that LLM coding agents suffer &apos;constraint decay&apos;: performance drops 30+ points when forced to follow architectural patterns, use specific databases, and integrate ORMs. Data-layer defects drive 45% of logic failures.</description><pubDate>Wed, 27 May 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>coding-agents</category><category>benchmarks</category><category>backend</category></item><item><title>LLMs Can Improve at Code by Training on Their Own Wrong Answers</title><link>https://stacksweep.dev/llm-self-distillation-code/</link><guid isPermaLink="true">https://stacksweep.dev/llm-self-distillation-code/</guid><description>Simple Self-Distillation (SSD) lets LLMs improve at code generation by training on their own unverified outputs, no correctness labels or execution environment needed. Qwen3-30B jumps 12.9 points on LiveCodeBench v6.</description><pubDate>Sun, 05 Apr 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>coding</category><category>training</category><category>llms</category></item><item><title>Claude Just Solved an Open Math Problem That Had Stumped Researchers for Weeks</title><link>https://stacksweep.dev/knuth-claude-cycles-open-problem/</link><guid isPermaLink="true">https://stacksweep.dev/knuth-claude-cycles-open-problem/</guid><description>Don Knuth published a paper describing how Claude Opus 4.6 solved an open combinatorics problem he&apos;d been working on for weeks: finding a general decomposition of a 3D digraph&apos;s arcs into three directed Hamiltonian cycles. Claude found it in about one hour across 31 explorations.</description><pubDate>Sun, 08 Mar 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>ai</category><category>agents</category><category>reasoning</category></item><item><title>Most Coding Agents Break 75%+ of Their Own Fixes Over Time</title><link>https://stacksweep.dev/swe-ci-coding-agent-benchmark/</link><guid isPermaLink="true">https://stacksweep.dev/swe-ci-coding-agent-benchmark/</guid><description>SWE-CI is a new benchmark that evaluates coding agents on long-term codebase maintenance via continuous integration loops, not one-shot bug fixes. Most models introduced regressions on 75%+ of tasks. Only Claude Opus exceeded a 50% zero-regression rate.</description><pubDate>Sun, 08 Mar 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>agents</category><category>benchmarks</category><category>coding</category></item><item><title>The Answer Key Trick That Cuts Reasoning LLM Training Time in Half</title><link>https://stacksweep.dev/a-star-po-faster-rl-llm-training/</link><guid isPermaLink="true">https://stacksweep.dev/a-star-po-faster-rl-llm-training/</guid><description>A*-PO is a new RL training algorithm for LLMs that precomputes an &apos;optimal value&apos; offline, then trains with just one sample per prompt instead of many. It matches or beats PPO and GRPO at up to 2x faster speed and 30%+ lower memory.</description><pubDate>Sun, 01 Mar 2026 00:00:00 GMT</pubDate><category>ai</category><category>research</category><category>llm</category><category>training</category><category>reinforcement-learning</category></item><item><title>Claude Adds Ability to Import Memory From Other AI Providers</title><link>https://stacksweep.dev/claude-import-memory-from-providers/</link><guid isPermaLink="true">https://stacksweep.dev/claude-import-memory-from-providers/</guid><description>Anthropic added a memory import tool that lets you copy your context and preferences from ChatGPT, Gemini, or any other AI provider into Claude in under a minute.</description><pubDate>Sun, 01 Mar 2026 00:00:00 GMT</pubDate><category>ai</category><category>claude</category><category>ai</category><category>anthropic</category><category>memory</category></item><item><title>LLMs Can Now Figure Out Who&apos;s Behind Any Pseudonym — For Just $4</title><link>https://stacksweep.dev/llm-deanonymization-pseudonymity/</link><guid isPermaLink="true">https://stacksweep.dev/llm-deanonymization-pseudonymity/</guid><description>Researchers from ETH Zurich and Anthropic show that LLM agents can re-identify pseudonymous online accounts at scale — achieving up to 68% recall at 90% precision compared to near 0% for the best classical methods. The assumption that posting under a pseudonym is safe no longer holds.</description><pubDate>Sun, 01 Mar 2026 00:00:00 GMT</pubDate><category>ai</category><category>privacy</category><category>security</category><category>research</category><category>llm</category><category>agents</category></item><item><title>Block Announces Layoffs of 4,000 People, Over 40% Cut</title><link>https://stacksweep.dev/block-layoffs-4000-ai/</link><guid isPermaLink="true">https://stacksweep.dev/block-layoffs-4000-ai/</guid><description>Jack Dorsey announces Block is cutting over 4,000 employees — nearly half its workforce — citing AI-driven changes to how companies operate. The stock jumped almost 25% after hours.</description><pubDate>Fri, 27 Feb 2026 00:00:00 GMT</pubDate><category>industry</category><category>block</category><category>layoffs</category><category>ai</category><category>jack-dorsey</category><category>industry</category></item><item><title>Claude Offers Free 6-Month Claude Max Memberships for Open-Source Maintainers</title><link>https://stacksweep.dev/claude-free-max-open-source/</link><guid isPermaLink="true">https://stacksweep.dev/claude-free-max-open-source/</guid><description>Anthropic is giving up to 10,000 open-source maintainers free Claude Max 20x subscriptions for six months.</description><pubDate>Fri, 27 Feb 2026 00:00:00 GMT</pubDate><category>devtools</category><category>anthropic</category><category>claude</category><category>open-source</category><category>developer-tools</category></item><item><title>Claude Code Now Remembers What It Learns Across Sessions</title><link>https://stacksweep.dev/claude-code-auto-memory/</link><guid isPermaLink="true">https://stacksweep.dev/claude-code-auto-memory/</guid><description>Anthropic shipped auto-memory for Claude Code. Claude now persists project context, debugging patterns, and preferences across sessions without manual setup.</description><pubDate>Thu, 26 Feb 2026 00:00:00 GMT</pubDate><category>devtools</category><category>claude-code</category><category>ai</category><category>developer-tools</category><category>anthropic</category></item><item><title>Google Restricts AI Ultra Accounts Over OpenClaw OAuth</title><link>https://stacksweep.dev/google-restricts-ultra-accounts-openclaw/</link><guid isPermaLink="true">https://stacksweep.dev/google-restricts-ultra-accounts-openclaw/</guid><description>Google locked AI Ultra subscribers out of Gemini models for using OpenClaw OAuth, with no warning or explanation. Anthropic banned third-party access two days earlier.</description><pubDate>Sun, 22 Feb 2026 00:00:00 GMT</pubDate><category>devtools</category><category>ai</category><category>oauth</category><category>google</category><category>anthropic</category><category>security</category></item><item><title>The truth about AI and skill retention</title><link>https://stacksweep.dev/ai-skill-retention/</link><guid isPermaLink="true">https://stacksweep.dev/ai-skill-retention/</guid><description>A randomized trial found that developers using AI assistance scored 17% lower on a skills test without gaining any speed advantage. The finding matters, but the study design limits how far you can take it.</description><pubDate>Thu, 19 Feb 2026 00:00:00 GMT</pubDate><category>ai</category><category>ai</category><category>research</category><category>learning</category><category>productivity</category></item><item><title>Your Agents.md Might Be Making AI Worse</title><link>https://stacksweep.dev/agents-md-making-ai-worse/</link><guid isPermaLink="true">https://stacksweep.dev/agents-md-making-ai-worse/</guid><description>An ETH Zurich study tests whether AGENTS.md and CLAUDE.md files actually help coding agents. LLM-generated context files reduce success rates while adding 20%+ to costs. Human-written ones barely help.</description><pubDate>Wed, 18 Feb 2026 00:00:00 GMT</pubDate><category>devtools</category><category>devtools</category><category>benchmarks</category><category>research</category><category>coding-agents</category></item><item><title>Anthropic&apos;s Confusing Claude Subscription Policy, Explained</title><link>https://stacksweep.dev/anthropic-claude-subscription-switcharoo/</link><guid isPermaLink="true">https://stacksweep.dev/anthropic-claude-subscription-switcharoo/</guid><description>Anthropic updated its Claude Code docs to ban OAuth tokens from being used in third-party tools. The community exploded. Then Anthropic said nothing was changing.</description><pubDate>Wed, 18 Feb 2026 00:00:00 GMT</pubDate><category>ai</category><category>anthropic</category><category>claude</category><category>policy</category><category>claude-code</category></item><item><title>An LLM Benchmark Idea: Earnings Forecasting</title><link>https://stacksweep.dev/llm-benchmark-earnings-forecasting/</link><guid isPermaLink="true">https://stacksweep.dev/llm-benchmark-earnings-forecasting/</guid><description>A proposed LLM benchmark: feed a model pre-earnings data, have it forecast the results, compare to actual. Here&apos;s why it&apos;s worth building — and why today&apos;s models make it more interesting than ever.</description><pubDate>Wed, 18 Feb 2026 00:00:00 GMT</pubDate><category>ai</category><category>ai</category><category>benchmarks</category><category>research</category><category>finance</category></item><item><title>New Study: Businesses Are Replacing Freelancers with AI at a 97% Cost Savings</title><link>https://stacksweep.dev/ramp-ai-replacing-freelancers/</link><guid isPermaLink="true">https://stacksweep.dev/ramp-ai-replacing-freelancers/</guid><description>A Ramp study using real firm-level spending data finds businesses are rapidly substituting freelancers for AI — with the heaviest spenders seeing $1 of AI replace $33 of freelance labor.</description><pubDate>Wed, 18 Feb 2026 00:00:00 GMT</pubDate><category>ai</category><category>ai</category><category>labor-market</category><category>research</category><category>freelancing</category></item><item><title>AI-Generated Agent Skills Are Pointless</title><link>https://stacksweep.dev/skillsbench-ai-generated-skills/</link><guid isPermaLink="true">https://stacksweep.dev/skillsbench-ai-generated-skills/</guid><description>SkillsBench tests whether structured knowledge packages improve LLM agents across 84 tasks. Curated Skills add 16pp. Self-generated Skills add nothing, or make things worse.</description><pubDate>Wed, 18 Feb 2026 00:00:00 GMT</pubDate><category>ai</category><category>agents</category><category>benchmarks</category><category>research</category><category>llm</category></item></channel></rss>