AI News Aug 19, 2026

industry

Enterprises are overpaying for simple AI queries — Snowflake's gateway now auto-routes to cut costs up to 3x

Enterprise teams running AI agents at scale are finding that a single model handles every task poorly — either the model is too expensive for simple questions or not capable enough for hard ones. Model routing, which picks the right model for each task automatically, is becoming the fix. Snowflake’s Cortex AI Gateway now offers dynamic model routing to address that: enterprises can select “auto” instead of a fixed model, and the system routes each task to whichever model offers the best combination of quality and cost. Snowflake said the capability can cut token costs by as much as 3x on some workloads — a figure from the company’s own internal testing — after finding that simple questions were often handled by its most capable model, making responses more expensive and slower than necessary. The move lands amid a broader industry shift toward automated model routing. Databricks, AWS, Google Cloud and Nvidia have all announced some form of model routing technology. Snowflake argues that model routing is more complex than just price and performance, it's also about governance and context. "For high quality, enterprise grade agents to be built, it's crucial to get the context and the governance right," Baris Gultekin, vice president of AI at Snowflake, told VentureBeat. “Context, trust and model choice all go hand in hand." Two mechanisms decide where a task goes The capability builds on Cortex AI Gateway, which Snowflake launched in July 2026 as a governance layer for agent and model traffic. Before dynamic routing, model selection ran off a static list per task rather than a true fallback system, Gultekin said. Dynamic routing itself runs on two mechanisms, according to Gultekin. A small model tries first. Under what Snowflake calls an advisor pattern, a smaller model attempts a task first. If it cannot finish the job, it calls a larger model as a tool and continues from there. A classifier sorts by task history. A separate classifier, trained on past queries, automatically routes straightforward questions to simpler models. Customers can still pin a model. Auto routing is optional. Customers can restrict routing to one model or a defined set of models, and the system routes only within that boundary. There is no separate fee. Snowflake prices AI purely on token usage. Routing to a cheaper model produces a cheaper bill, with no additional charge for the routing decision itself. Access controls follow the task, not just the data Snowflake ties routing to the same access controls it already uses for data governance. Governance starts at the data level with role-based access controls. It extends to models next, where customer roles map to buckets of approved models. It extends again to agents, where an agent can be restricted to narrower privileges than the user invoking it. Open models can run from a customer's own region to satisfy data residency requirements. Gultekin said all inference, open and proprietary alike, stays inside Snowflake's security boundary rather than routing out to an external provider. That regional and perimeter setup matters specifically for open models with non-U.S. origins, including DeepSeek-V4-Flash and GLM-5.3, both developed in China. Snowflake's recent acquisition of Natoma adds another layer. The deal brings more than 100 MCP connectors with scoped, governed access. An agent could get read-only access to a connected tool like email, for example, rather than broader permissions. Context lets a cheaper model do the work Snowflake recently announced its Horizon Context and Cortex Sense tools that provide context capabilities. Without good context, a model has to do the exploratory work itself, writing and testing SQL, searching through data and retrying when something does not work. Gultekin explained that the process is expensive, and getting it right typically requires a more capable model. Packaging the context in advance removes that exploratory step, which means a simpler, cheaper model can often handle the same task. Snowflake also builds agent memory into that context. As an agent is used repeatedly, its memory updates and gets folded back into future queries. The system does not re-solve the same problem from scratch each time. Memory becomes part of the context passed to the model. OpenRouter, Databricks and Nvidia are chasing the same problem There is no shortage of technologies in the model routing space. OpenRouter is one of the most widely known options, providing a platform that enables organizations to route based on cost and performance. Nvidia on August 11 announced Switchyard as a technology layer to help route AI model choice . Databricks has an offering as well with Smart Routing for its Unity AI Gateway. "The interesting part is what it says about where differentiation has moved," Sanjeev Mohan, Principal and Founder, SanjMo, told VentureBeat. "Snowflake isn't really selling routing, it's selling routing that never leaves the governed data boundary, with access controls, tagging, and cost attribution already attached." Mohan added that for a company whose data and compliance already center on Snowflake, routing that keeps data in place and attributes spend by team is a real lever on that problem. For a company without that center of gravity, a neutral gateway may route across more models with less friction. Mohan frames the market as three distinct camps rather than one competitive field. Databricks approaches governance from data engineering and ML lineage. Its Unity Catalog governs data, models and pipelines for teams building and training models. Snowflake approaches governance from analytics and access control, governing who can touch which data and attributing usage across business units. A third camp includes neutral gateways such as OpenRouter, LiteLLM, Portkey and hyperscaler routers like Azure AI Foundry. These compete on model breadth and avoiding lock-in rather than deep governance. Choosing a router means choosing a governance model Model routing is now table stakes for enterprises. The decision that matters is which governance model already fits how their data and teams are organized, not which vendor’s router is fastest or cheapest. Manual model selection is becoming a cost liability at agent scale. What worked when a team ran a handful of agents breaks down at scale. Hundreds of agents making routine model calls with no automated cost check in place adds up fast. Evaluate the governance model, not the router's feature list. The real question, per Mohan, is which governance model matches the data estate already in place, and which one gives the cost visibility needed to avoid an unpleasant surprise. The right starting point depends on where an enterprise's data already lives. A Snowflake shop gets more value from in-platform routing that respects its existing access model and bills back to cost centers than from raw model breadth, according to Mohan. A Databricks-centric team worried about lineage across training and deployment is better served by a gateway built around that same lineage. A multi-platform or model-first team that wants maximum choice with minimal lock-in fits better with a neutral gateway, the same pitch behind OpenRouter's valuation. "For a practitioner, don't start with the router, start with where your governed data and platform commitment already live, and with how exposed your margins are to inference cost," Mohan said.

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official

Asana cleared 5 years of engineering work in 2 weeks with Codex

Asana used OpenAI Codex to replace an outdated testing system in two weeks, completing work expected to take five years for about $12K.

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research

SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization

arXiv:2608.14579v1 Announce Type: new Abstract: Logic synthesis optimization poses significant challenges due to exponentially growing search spaces, sparse reward signals, and diverse logic structures. Traditional expert-designed flows lack adaptability, while reinforcement learning (RL) methods often suffer from low sample efficiency and limited interpretability. We introduce SKILL, a Self-correcting Knowledge-guided Iterative Large Language Model Agent that unifies multi-agent LLM reasoning and RL-based environment interaction for automated synthesis optimization. SKILL coordinates three specialized LLMs: GPT-4o for strategic planning, Claude Sonnet 4 for detailed reasoning, and Gemini 2.5 Pro for efficient analysis with a PPO-based RL agent that learns actionable policies through direct interaction with synthesis tools. A novel self-correcting module monitors environment feedback (PDA metrics), detects suboptimal behaviors, and invokes LLM-guided recovery strategies. Evaluations on IWLS, OpenCores, and EPFL benchmarks show SKILL achieves a 12.4 % PDA improvement over expert flows and 86.3% success rate on logic systems up to 500K gates.

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open-source

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

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video

Qwen3.8-27B & How to Serve it Fast

In this video, I look at the long awaited Qwen3.8-27B model. Both what it can do and how to serve it at the maximum tokens per second Thanks to Dell for Sponsoring the Compute #DellProPrecision #DellProMax #DellTech #NVIDIA 📖 Website: https://qwen.ai/ 🤗 HF: https://huggingface.co/collections/Qwen/qwen38 SGLang: https://lmsysorg.mintlify.app/cookbook/autoregressive/Qwen/Qwen3.8-27B Twitter: https://x.com/Sam_Witteveen 🕵️ Interested in building LLM Agents? Fill out the form below Building LLM Agen

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industry

Claude Code gets a /design command that lets developers create UI mockups right in the terminal

With the /design command, Anthropic brings a visual design workflow directly into Claude Code. Developers can generate UI mockups as artboards right in the terminal before writing any code. Claude reads the existing codebase and matches the current UI style. The article Claude Code gets a /design command that lets developers create UI mockups right in the terminal appeared first on The Decoder .

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china

6个Agent组团Vibe Gaming：自己生成、试玩、修Bug

代码能跑≠游戏能玩

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open-source

mukul975/Anthropic-Cybersecurity-Skills

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community

I built a visual architecture & token-reduction diagram engine for multi-agent LLM pipelines

When working with multi-agent LLM systems, the hard part usually isn't getting a response—it's knowing what actually happened under the hood: which model handled what, what was sent over the network, how much it cost, and whether sensitive data was masked before leaving your machine. To solve this, I added a visual diagram engine to **Mova Context** in this latest release, allowing you to generate a complete architecture map with a single command: `mova run --diagram`. Here is a real example output generated from a customer data compliance project running hybrid agents (**Local Ollama + Cloud Gemini**): context diagram * **Visual Diagram Engine:** Generates real-time architecture and execution maps using OpenType vector font rendering with WCAG AA contrast standards (clean export to PNG and PDF). * **Cross-Channel Tracing:** Added execution tracing across CLI, Chat, MCP, and HTTP API with an explicit `[THIS RUN]` indicator. * **Hybrid Execution Breakdown:** Visualizes local agents (`llama3.2:3b` via Ollama) running alongside cloud agents (`gemini-3-flash-preview`) in the same execution group. * **PII & Privacy Tracking:** Identifies per-agent status for PII Masking and explicitly tracks how many tokens were pseudonymized before leaving your local network. * **Cost & Token Transparency:** Explicitly flags local execution as `$0.00 (local — no cost)`, while displaying estimated USD costs for cloud agents calculated *after* context reduction. * **Token Reduction Pipeline:** Breaks down token overhead by source (prompts, skills, focus files, engine overhead) and displays the total percentage saved. * **Bilingual Docs:** Fully updated documentation (`README.md` and `COMMANDS.md`) in both English and neutral Spanish. The project is **100% open source** written in Go. * **GitHub Repo:** https://github.com/m1guel1982/mova-context If you find it useful for structuring, auditing, or optimizing token budgets in your agentic workflows, feel free to check it out, star the repo, or drop feedback in the comments! submitted by /u/1982_miguel [link] [comments]

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industry

Google’s Pet Memory forgot who my cats are

One of the best things my smart home does is help me care for my pets, and security cameras are particularly useful for keeping track of my many critters. But the barrage of notifications they send often means I miss important ones. So, when Google announced its new Pet Memory feature for Gemini for Home, […]

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official

The builder’s guide to GPT‑5.6

Learn how startups use GPT-5.6 to build faster, more cost-efficient AI agents with smarter model selection and new Responses API capabilities.

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open-source

akitaonrails/ai-memory

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community

Context is becoming more important than the prompt

Feels like a lot of prompt engineering problems are really context problems since you can keep refining the prompt but if the model doesn't understand the project or what you're trying to accomplish you're still explaining half the situation every time. I'm starting to think giving an agent persistent context is more useful than constantly trying to write the perfect prompt since the more it knows the better the results. submitted by /u/ContractBoth4254 [link] [comments]

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community

Giving employees ChatGPT access isn’t the same as AI adoption

Most companies don’t have an AI adoption strategy. They have a few employees who got good at AI on their own. That can look like progress from a distance. Look closer and you often find no shared standard for what “good” AI use looks like, no consistent way to measure skill, and employees quietly using personal AI accounts because access at work is limited. We’ve seen this pattern repeatedly in AI proficiency assessments across dozens of organizations. When employee skill levels are plotted across 10 levels, most people cluster around Levels 1 and 2. That includes teams that have had access to AI tools for two years or more. That makes sense, because most employees have full time jobs and can’t spend hours every day testing models, learning new prompting methods, and keeping up with every new capability. So, they learn when they can while AI keeps changing. That creates a bigger issue than individual skill. Leaders can see employees using AI and assume adoption is happening. Usage alone doesn’t tell you whether people are getting meaningful, repeatable results. Recent research points to a similar disconnect. Executives tend to be far more optimistic about AI progress and ROI than the middle managers responsible for making it work inside everyday processes. A better question for leaders is: Do we know how proficient our people actually are? If the answer is no, measuring usage is probably giving you an incomplete picture. Assess proficiency first. Find out where people are struggling, then give them a shared method for improving. That’s when AI starts becoming an organizational capability instead of something a handful of employees figured out for themselves. For anyone interested in the longer discussion, John Munsell recently talked through the assessment approach, proficiency heat maps, and what we’ve learned from measuring AI skills across organizations: https://youtu.be/zY24em_Q3OM?si=5gozpZa8Ae-wS0Vs submitted by /u/Admirable_Phrase9454 [link] [comments]

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community

I brought an ancient Zen book to life with Claude

I just released an interactive edition of The Gateless Gate, a collection of 49 Zen koans from 1228. Every koan gets its own 3D scene, with its own soundscape and a full spoken reading. Other than the speech, it's all generated procedurally on startup, so there's nothing to download. Demo: https://killedbyapixel.github.io/GatelessGate/ It started as a test. I had this idea to build out these scenes in black and white with a red accent object. So I built a couple of scenes with Claude just to see whether the idea worked at all, and it turned out so well that I decided to do the whole book. Claude created placeholder scenes for all the koans based on what was in them, then I did many passes over them tweaking the composition, the camera, the lighting, the models themselves. Nothing shipped the way it was generated, but having something already standing there to react to made all the difference. There are a lot of small effects in there too. Everything is drawn with an ink outline over a paper grain, and page turns dissolve like wet ink spreading through paper. For atmosphere there's rain, snow and ocean. The grass and foliage move with the wind, water leaves ripples where you touch it, and the ocean picks up when it's windy. The red objects are also interactive but you'll have to find out for yourself what they do. We built a lot of custom tools along the way, like a model viewer for looking at every 3D model on its own, and a developer mode you can toggle by pressing HOME if you want to poke at the settings. All of the sound is generated too, with no samples anywhere. For the narration we ran a voice audition. We had a batch of different voices reading the same lines and went with a British one in the end. That whole pipeline is automated now, so when better speech models come along I can re-voice the entire book with one command. There is also a dynamic soundscape that goes along with each scene. The text needed work as well. The English translation was from 1934, and the preface and afterword had no public domain English at all, so we translated those from the original Chinese. I modernized the rest where the old wording had stopped making sense. A lot of words just don't read the way they used to. It took over a week of pretty intense work, and it's the most art direction I've ever done on anything. Everything in it is tuned by hand. It feels really good to see it finished and my vision brought to life. submitted by /u/Slackluster [link] [comments]

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developer-tools

Markdown SVG upgrades

I started building my markdown-svg-renderer tool in May , but I've since added enough features to it that it's worth talking about here again. It's evolved into my ideal tool for sharing Markdown transcripts that include SVG documents. Given my proclivity for drawing pelicans riding bicycles this is a problem that I needed to solve! The tool is very simple. Navigate to markdown-svg-renderer in your browser and paste in some Markdown to see it rendered... or save that Markdown to a CORS-friendly URL or a GitHub Gist and paste in a URL to that document. The URL option will give you a bookmarkable page, for example https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F6f9e48293be5c916652d29f0dc0b0657 - which bakes in the URL to this Gist . If you visit the Gist you'll see raw SVG: In the rendered tool that looks like this instead: As you can see, that SVG block in the Markdown has been transformed into a rendered SVG (in this case animated) plus several tabs. The tabs are the really fun bit. The PNG and JPEG tabs render that SVG to those image formats in the browser and lets you copy or download them - useful for sharing on platforms that don't support SVG directly. The MP4 tab is new today - it examines the SVG to see if it contains any animations, attempts to guess how long the looped video should be, then renders a whole bunch of frames of the animation and loads 30+MB of ffmpeg.wasm so it can compile those frames into an MP4 video using the full power of FFMPEG compiled to WebAssembly and running in the browser. Being able to turn an animated SVG into a MP4 again makes it easy to share on platforms that can't support SVG animation natively. It's a neat trick! Tags: svg , markdown , tools

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open-source

harry0703/MoneyPrinterTurbo

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community

Fresh ChatGPT chats are fast. Established ones now take 10–40 minutes or fail (HAR data)

Long post, but there’s a TL;DR first. I’m writing it this way because a vague “ChatGPT is slow” post wouldn’t be useful. I’ve also included the HAR measurements for anyone who wants the technical details. TL;DR Since around August 17–18 , my established ChatGPT conversations have suddenly become dramatically slower and much less reliable. I’m not only talking about longer Thinking time. I’m seeing: 10–20+ minutes just for the final answer to stream sometimes 30–40 minutes of streaming 20+ minute Thinking or Analyzing phases answers starting normally and then stalling failures during Thinking failures halfway through the final answer retries failing again at a different stage apparently dead requests sometimes recovering much later very long requests eventually failing after I’ve already waited one or two hours Sometimes I can retry the same interaction several times and still not get one complete answer. Fresh chats are usually much faster. My largest Project chats are admittedly heavy. I use a file-based continuity/checkpoint system because some projects run for months, conversations eventually become too large or lose context quality, and I need to move into a new chat without losing the whole project state. So yes, I expect those chats to be slower than a blank conversation. But this workflow already existed, and those chats were already large. It was never remotely this bad before August 17–18. Moving to a continuation chat also used to restore useful performance. This time, the new continuation felt much better for perhaps 10–15 minutes , then suddenly collapsed into the same near-unusable state. I’ve tested Chrome, Edge, Incognito, iPhone, a cellular hotspot, hard refresh, signing out and back in, and clearing ChatGPT site data. Nothing meaningfully fixed it. I also captured HAR diagnostics. In one comparison using GPT-5.6 Extra High: Affected established/Project chat ~22.9 seconds to first output ~90.7 seconds total ~1.60 seconds median recorded stream interval Fresh-chat control ~2.35 seconds to first output ~22.7 seconds total ~0.20 seconds median stream interval Another heavier request contained a single ~7 minute 47 second stall , then later resumed and completed. I’ve already sent the HARs and troubleshooting results to OpenAI Support, and the case has been escalated to a specialist. I’m not asking Reddit to replace Support. I’m trying to find out whether this pattern is affecting other people and whether anyone has found a practical workaround. Once waiting, failures, partial answers and retries are combined, the real time required to obtain one complete usable response can approach an hour or worse. For an interactive tool, that’s not workable. Why my chats are heavy, and why “start a new chat” used to be the solution I use ChatGPT for ongoing personal and technical projects rather than treating every conversation as disposable Q&A. Over time, those chats accumulate decisions, requirements, research, files, tool activity, failed approaches and other project history. Eventually a chat can become too large or its context quality can deteriorate. I already knew that. That’s why I built an interim continuity system. Important project state is stored in versioned/checkpoint files, so when a conversation reaches its practical limit I can: preserve the current state → move to another chat → restore the project → continue It’s a temporary solution, and I know it carries more overhead than a properly engineered persistence and retrieval system would. I’m still relatively new to software development and use ChatGPT and Codex partly to learn and build tools I wouldn’t yet know how to create alone. But the key point is that this migration process used to work. Previously, an old chat would gradually become too heavy, I would migrate, and the continuation chat would be properly usable again. The latest migration behaved completely differently. One of my largest Project conversations became effectively unusable, so I moved into what is roughly the fourth continuation chat for that project. At first, the improvement was immediate. If a completely blank chat is 100% usability, the continuation initially felt subjectively around 70–80% usable . That was completely acceptable, because it already started with a meaningful amount of restored state. Then, perhaps 10–15 minutes later , it fell off a cliff. Not after weeks of additional conversation. Not after hundreds of new messages. It went from “finally, I can work again” to something that felt closer to 5% usable . Those percentages obviously aren’t benchmarks. They’re just the clearest way I can describe how dramatic the change felt. There is a fair counterargument: this is continuation chat number four. Every continuation starts heavier because more state and files have accumulated. That absolutely could explain why each one reaches its performance limit sooner. I think that’s part of the story. It just doesn’t explain the sudden magnitude of the change. The Project was already large. The continuity system already existed. I was already using this workflow. Then, around August 17–18, it became catastrophically worse. And it isn’t limited to that one giant Project. I’m also seeing serious slowdown and failures in ordinary established conversations, smaller Projects and things like travel-planning chats. The giant chats are the worst, but they’re not the only ones affected. What makes it unusable is the combination of latency and failure If ChatGPT had simply become consistently slower, I could at least plan around it. That isn’t what I’m getting. A request can fail during Thinking after I’ve already waited a long time. Another can finish Thinking, start streaming the answer, and then fail halfway through after another 10, 20 or even 30–40 minutes. Retries can fail too, and not necessarily at the same stage. One attempt dies during Thinking. The next gets through Thinking but fails halfway through the answer. The next stalls somewhere else. I’ve had cases where something like five attempts around the same interaction still failed to produce one clean completed response. That’s why “response time” no longer describes the actual problem. The useful metric is how long it takes to obtain one answer that actually finishes. For example: Attempt 1: 20 minutes Thinking → fails Attempt 2: 10 minutes Thinking → 25 minutes streaming → fails halfway Attempt 3: 15 minutes → finally succeeds Technically, the successful response took 15 minutes. In reality, I spent more than an hour getting it. I haven’t collected enough controlled attempts to claim the mathematical average is exactly one hour. What I’m saying is that once Thinking, tool work, streaming, failures and retries are combined, the effective wall-clock time to obtain one usable answer can approach an hour or worse. Sometimes it’s much faster. Sometimes it’s worse. The unpredictability is almost as damaging as the latency itself. I also can’t reliably hide the delay by using multiple chats. Normally I could send a heavy request in Chat A, switch to Chat B and do something useful, then come back later. Now I can return and find that Chat A stalled halfway through the answer while Chat B failed during Thinking. Parallel chats don’t help much when each request has a meaningful chance of failing independently. The actual answer stream is especially strange. Waiting for difficult reasoning is one thing. If I ask Extra High to research something complicated or inspect many files, I expect it to take time. What feels broken is when Thinking appears to be finished and the final answer itself takes 10–20+ minutes to stream onto the screen . I’ve also seen cases around 30–40 minutes , and the response can still fail before reaching the end. This happens on both ChatGPT Web and the iPhone app. That made me wonder whether Chrome already had the completed answer and was simply struggling to render a giant page, so I captured HAR/network diagnostics. What the HAR captures showed I’m not a network engineer, and I’m not pretending a HAR file reveals OpenAI’s internal root cause. It does provide objective timing data. The comparison below used the same account, PC, Chrome session, GPT-5.6 Thinking model and Extra High setting. Important caveat: the slow test was an established Project chat with accumulated context, files and tool history . The control was a fresh ordinary chat. This is not a perfect apples-to-apples benchmark, and the Project state may be directly relevant. Affected established/Project chat The prompt itself was tiny. ~22.94 seconds to first output ~90.71 seconds total request time ~1.60 seconds median recorded stream interval Fresh-chat control I asked for about 500 words explaining how modern SSDs work without web research. ~2.35 seconds to first output ~22.71 seconds total ~0.20 seconds median recorded stream interval So the established chat was roughly: 9.75× slower to first output 4× slower overall about 8× worse in median stream timing despite the fresh control requesting a substantially longer final answer. That’s why I no longer think the slow visible response is purely the browser struggling to draw already-generated text. At least in that capture, the incoming stream itself was substantially slower. A second HAR captured a different failure pattern. This was a genuinely heavy file-processing request, so I’m not claiming it should have completed quickly. The whole request took about 10 minutes 48 seconds . The telemetry included one maximum interval of roughly 467 seconds , or 7 minutes 47 seconds . The turn looked effectively dead, then suddenly resumed and completed. During that pause, ChatGPT’s WebSocket remained open, normal ChatGPT traffic continued, and another conversation in the same browser session kept receiving updates. So in that incident, the whole browser, network connection and ChatGPT session had not frozen. One individual turn stalled for almost eight minutes and later recovered. That creates another practical problem: when nothing has happened for seven minutes, I have no reliable way to know whether the request is still working, temporarily stalled, going to finish in two hours, or already dead. Some other extreme examples I’ve seen, these are observations rather than controlled benchmark results: A Pro request took roughly 106 minutes and eventually completed. Another appeared to run for around 180 minutes . The UI showed a problem, I moved on, and much later its completed answer appeared anyway. More recently, one request ran for roughly two hours and then failed . I edited and resubmitted it; the retry then spent roughly 40 minutes streaming the final answer . At around the same time, another conversation spent roughly 20 minutes in Thinking before producing anything. What I’ve already ruled out I’ve tested: Chrome Chrome Incognito Microsoft Edge ChatGPT on iPhone my normal internet connection the PC connected through an iPhone cellular hotspot hard refresh signing out and back in clearing ChatGPT site data Fresh chats generally remain much faster. Affected established chats remain much worse. Changing browser or network path didn’t restore the previous behavior, and the issue appears on mobile too. So while accumulated context clearly matters, a broken Chrome profile or bad home Wi-Fi doesn’t explain the full pattern very well. My current suspicion is that two things are interacting: Accumulated conversation, Project and file state naturally adds overhead. Something recently changed that made established or state-heavy contexts perform far worse than before. The same general projects, files and workflow were usable before August 17–18. Long chats would eventually slow down, but migrating to a continuation restored useful performance. Now even migrated chats can go from reasonably usable to nearly unusable very quickly, while smaller established chats are also showing much worse latency and failure rates. Conversation size clearly makes the problem worse. I just don’t think it explains why the same general workload suddenly became this much slower and this much less reliable . Why this matters to me, and what I’m asking A lot of what I do is iterative: idea or problem → reason through it in ChatGPT → turn it into something actionable → use Codex when implementation is needed → inspect the result → return to ChatGPT for the next analysis or debugging step I don’t expect every answer in five seconds. A genuinely difficult Extra High task taking several minutes is completely reasonable. But right now, in badly affected chats: a heavy Extra High interaction can effectively consume 30–60 minutes a more ordinary interaction can still take 8–15 minutes neither duration guarantees the answer will actually finish Once retries enter the picture, the real time can be much worse. Pro mode in those chats is something I barely want to use right now because it’s already intentionally slower, and I have no idea whether I’m starting a 20-minute job, a two-hour job, or something that eventually fails. I’m on the $100/month Pro plan . I’m not saying that makes giant workloads free or instant. I do think it’s reasonable to expect a premium interactive product to remain interactive enough to use. I’ve already provided OpenAI Support with sanitized HAR captures, timestamps, affected-chat information, comparison measurements and troubleshooting results. The case has been escalated to a specialist. I’m not posting the raw HARs or request IDs because they contain private/internal metadata. What I’d like to know from other users: Are you seeing this too, especially since August 17–18? Does it affect Projects, normal chats, or both? Are fresh chats fast while established chats are slow? Which model/effort level, plan and client are you using? Have you found any workaround that preserves useful context? Right now every Send button in an established chat feels like a slot machine: 30 seconds? 10 minutes? 40 minutes of streaming and then failure halfway through? 20 minutes Thinking and then an error? Five retries? Two hours and then failure? Looks dead for eight minutes and suddenly comes back? No idea. When all of those outcomes are combined, the effective time required to get one successful complete answer can approach an hour or worse. If anyone else is experiencing this, I’d really like to compare notes. And if someone from OpenAI sees this, I already have an escalated Support case with diagnostic evidence and I’m happy to run specific controlled tests that could help isolate the problem. submitted by /u/Embarrassed-Spite541 [link] [comments]

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china

DeepSeek Harness Hands-On: Four Work Modes, 'Model + Harness = Agent', and the Most Ambitious Agent Open Source of the Year

DeepSeek Harness launched its developer preview and open-sourced the code at 8:30 PM on August 13. A first-night hands-on review finds the product shell still early at v0.1 but the architecture ambition the biggest of the year: four preset work modes, an everything-is-a-plugin philosophy, and the equation Model + Harness = Agent.

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developer-tools

alchemy-utils 0.1a0

Release: alchemy-utils 0.1a0 I've long pondered what a database agnostic version of my sqlite-utils Python library and CLI utility might look like. This morning (literally a shower project) I tasked Codex and GPT-5.6 Sol Ultra with building a prototype: Do a research spike to see what it would take to build a library with the same core API as SQLite-utils - in particular the insert and upsert and insert_all and upsert_all and create and update methods, and the table introspection stuff - but backed by SQLalchemy so it works for multiple database engines Test against PostgreSQL and SQLite and duckdb Use ~/dev/sqlite-utils for reference Create a git repo for this and commit and early and often - use uv init to start the project - use red/green TDD and pytest, see ~/dev/django-sql-dashboard for one idea as to how the PostgreSQL tests could work It took very few follow-up prompts to produce this project in a state good enough to release as an alpha. Here's a one-liner I can use to list the rows in a table in my local PostgreSQL copy of my blog's database: uvx --with 'alchemy-utils[postgresql]' alchemy-utils rows 'postgresql+psycopg://simon@localhost:5432/simonwillisonblog' redirects_redirect The output from that starts like this: [ { "id": 2328, "domain": "simonwillison.net", "path": "2020/May/21/apple-photos-sqlite/", "target": "/2020/May/21/dogsheep-photos/", "created": "2020-05-21T13:03:46.591692-07:00" }, { "id": 3, "domain": "feeds.simonwillison.net", "path": "swn-links", "target": "https://simonwillison.net/atom/links/", "created": "2017-10-01T14:12:54.820729-07:00" } Or if you'd like a DuckDB database with every tree in San Francisco , schema created automatically to match the file: curl 'https://raw.githubusercontent.com/simonw/sf-tree-history/refs/heads/main/Street_Tree_List.csv' | uvx --with 'alchemy-utils[duckdb]' alchemy-utils insert 'duckdb:////tmp/trees.db' trees - --csv (That one took nearly an hour the first time I ran it, so I had Codex optimize it and got it down to around 35 seconds.) Tags: databases , postgresql , projects , python , sql , sqlalchemy , sqlite , sqlite-utils , duckdb , coding-agents , codex

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community

Antrophic Employee said there is "make a lot of money" button

I very much believe he is correct. The main issue is that "make a lot of money" button works only for existing businesses, with large enough audiences to make a lot of money by baking integrations, MCP for agents into Claude Code plugins or other AI workspaces and charging AI users for usage. What is missing, is a fair discovery and execution engine, that would allow non-corporations to participate. Without convincing user to put card details on some random-startup.ai website. Without forcing users to go through checkout process and pay $29 sub just to run random feature they need for few days. Not to mention configuring integration. Anyways, have anyone tried pressing that button? Did it work? submitted by /u/EagleApprehensive [link] [comments]

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community

Codex Patched My Samsung Odyssey G9 Firmware to Add a New Gaming Crosshair

I hated the built in crosshairs my monitor came with and Samsung did not include a way to make changes. So I asked Codex to make a new firmware update to change the crosshair to a dot. It took about 15 minutes and came back with a new file. Codex explanation: I started with Samsung’s original 1008.2 firmware and made a very small binary patch rather than attempting to rebuild or substantially rewrite the firmware. What the patch changes 1. Custom version number The version reported to the firmware updater was changed from 1008.2 to 1009.3. This is a custom, made-up version number. It is not an official Samsung 1009.3 release. The higher number allows the monitor to recognize the file as an upgrade rather than rejecting it as the same or an older firmware version. 2. Tiny center dot The firmware contains six selectable Virtual Aim/crosshair options. I redirected all six options to a small 7×7-pixel graphic that already existed inside the firmware and positioned it at the center of the screen. The result is a simple, unobtrusive center dot using the monitor’s own hardware overlay. It does not require a game overlay, desktop application, ReShade, or anything else running on the computer. Technical details Original firmware: M-C9557GGPA-1008.2 Custom firmware: M-C9557GGPA-1009.3[1A93].img Total binary difference: 13 bytes The patched image passed the build and checksum validation performed on the computer. The monitor accepted the firmware, restarted normally, and the modified Virtual Aim options display the new center dot as intended. Important warning This is unofficial firmware and is not supported or approved by Samsung. A successful checksum only confirms that the file was modified as intended. It does not guarantee that flashing it is safe on every monitor, hardware revision, region, or previously installed firmware version. Installing modified monitor firmware always carries a risk of installation failure or, in the worst case, making the monitor unusable. Anyone experimenting with this should understand the recovery options and proceed entirely at their own risk. submitted by /u/manikfox [link] [comments]

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community

cannot do subagents/background agents in codex luna 5.6?

i used to be able to say "spin up a ux/ui agent to make a pass on this, then a code agent", and it would do background terminal stuff. this is on mac using the cli codex. now it says it cannot do that and previous gpt models did that. did something change? submitted by /u/dropDtooning [link] [comments]

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industry

Do you use a personal agent?

Give AI a complete history of your desktop activity

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developer-tools

llm-gemini 0.33

Release: llm-gemini 0.33 It's been a while since the last llm-gemini release. This version of the plugin adds support for today's Gemini 3.7 Flash release, plus gemini-3.6-flash , gemini-3.5-flash-lite and two embedding models gemini-embedding-2 and gemini-embedding-001 . The plugin is also upgraded for compatibility with LLM 0.32, which means you can now see reasoning traces and you can also enable server-side tools using this pattern: llm -m gemini-3.7-flash -T CodeExecution \ 'use python to calculate (factorial of 13) * 3' I had Gemini 3.7 Flash draw me some pelicans riding bicycles at high, medium, and low thinking efforts (minimal, which was an option in 3.6 Flash, has been removed in 3.7.) Here's the high level one, which is pretty great: Update 14th August 2026 : I had originally said that the SVG rendered incorrectly in Chrome and Firefox, and blamed Gemini 3.7 Flash for producing invalid SVG. That was entirely incorrect: the rendering glitch was my fault, caused by a bug In my rendering tool . I've now fixed that bug. Tags: google , ai , generative-ai , llms , llm , gemini , pelican-riding-a-bicycle , llm-release

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community

Anthropic’s Project Parka sits through meetings and assigns Claude agents the homework

submitted by /u/ryanmerket [link] [comments]

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community

Here's a prompt that turns raw numbers into a readable report without the usual AI report generator filler

Ask for a report and you get an intro about how important the topic is, then the numbers buried in a paragraph, then a conclusion that says "in summary." The reader wanted the finding in the first line. This prompt inverts that. Data: [PASTE NUMBERS / BULLET POINTS] Audience: [WHO reads this and what decision they make] Write a short report with this order: The single most important finding, in one sentence, first. Two or three supporting points, each starting with the number then what it means. One thing that looks off or needs a decision. Rules: no introduction about why the topic matters. No "in conclusion." Do not describe a number without saying why it matters to the audience. If the data does not support a claim, say "not enough data" instead of guessing. The rule that changes everything is "no introduction about why the topic matters." That opening is where almost all report filler lives. Tying every number to the audience's decision is the second half, it stops the model from listing figures nobody asked about. The "not enough data" clause is there because otherwise it will confidently interpolate a trend from two points. What do you use to keep these things from over-explaining, mine still occasionally slips a summary paragraph back in at the end. submitted by /u/raw-hit10 [link] [comments]

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community

Here's a prompt that makes you predict a paper's results before it lets you read the discussion

I'm a chemistry PhD, and my reading problem was never comprehension in the moment, it was that nothing stuck. I'd read a paper, feel like I got it, and retain nothing a week later. The fix that worked best for me borrows from how we actually learn at the bench: you predict what an experiment will do, then you find out you were wrong, and the surprise is what you remember. So instead of asking a model to summarize a paper, I use it to withhold. This prompt turns reading into a prediction game. You commit to an answer before the paper tells you, which forces the encoding that plain reading skips. I'm going to work through a paper with you. You have the full text; I do not want a summary. Paper: {{paste it, or the sections}} Run it like this: 1. Tell me only the research question and the setup: what they were testing and how. Stop there. 2. Ask me to predict, in my own words, what I think they found and why. Wait for my prediction. 3. Now reveal the actual result. Explicitly tell me where my prediction matched and where it was wrong. 4. For each place I was wrong, ask me why I think I got it wrong, then give me the paper's actual reasoning. 5. At the end, give me one sentence I should be able to recall in a week, phrased as "the surprising thing here was...". Do not reveal results before I've committed to a prediction. The point is for me to be wrong first. Being wrong on purpose is the whole mechanism. When your prediction misses, the correction sticks in a way a summary never does, because your brain had a stake in it. Works on review papers too, just predict the conclusion from the abstract and intro before reading the rest. submitted by /u/Ok_Layer_1947 [link] [comments]

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community

Building a world with my voice - A-Frame (Three.js, WebXR) + Claude Code

This is a project built with A-Frame (Three.js, WebXR) that lets me build virtual worlds with my voice, all from within my VR headset. My mic is hooked up to Claude Code, which in turn runs against the project codebase. Since the agent is working directly with code, the possibilities of what can be achieved are pretty broad, essentially being limited only by what the underlying LLM is capable of with WebXR. The agent can build anything from simple objects, to animations, to interaction capabilities, to whole environments in order to fulfil a request. This is still far from perfect. Latency is pretty high, taking anywhere from a minute to multiple minutes to carry out a change depending on how complex the request is. The codebase is also not very accommodating to anything that isn't a frontier model right now. From my testing, models like Haiku and Sonnet can't keep up, and Opus 5 only works with reasoning set to at least Low (most of the video is recorded with Opus 5 Low). But the foundation is now a reality, and as these models improve I can see this getting very capable very quickly. If you have ever imagined being able to put on a VR headset and transporting yourself to any world you want by simply asking, to shape that world to meet your creative vision, and to watch that world materialise around you, then in a small way, we are here. submitted by /u/Art_from_the_Machine [link] [comments]

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open-source

Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets

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community

NOTICE: BE CAREFUL WITH “DROP YOUR BEST PROMPT” POSTS

Many accounts post essentially the exact same questions every few months. Im not kidding, many of these are a 1:1 per token match on wording, phrasing and sentence structure. Same wording. Same request for people to hand over their best prompt tricks. There was a previous post that received hundreds of upvotes and a large number of responses. Now they're doing it again. I obviously cannot prove any of this, but at this point I would be careful about treating posts like this as innocent questions. When somebody repeatedly asks a large community to: “Give me your best prompts.” “Drop your secret tricks.” “What prompt 10x'd your results?” ...you may not be helping another user learn. You may be supplying material for content mining, prompt harvesting, engagement farming, newsletters, LinkedIn posts, courses, ebooks, datasets, or something else entirely. Again, I am not claiming that is definitely what this account is doing. But posting the same high-engagement fishing question again months later is weird enough that people should notice the pattern. Your prompts, workflows, techniques, and hard-earned little discoveries have value. Don't automatically dump them into every thread that asks. Sometimes the person asking the question may be less interested in the answer than in collecting the answers. Process disclosure: GPT-assisted, Google-researched, human-reviewed (HITL) --- EDIT: Just for perspective have a look at this: https://www.reddit.com/r/EdgeUsers/s/2JB9wy1Rks submitted by /u/Echo_Tech_Labs [link] [comments]

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community

The Downfall of a Vibecoder

submitted by /u/SuperiorDev [link] [comments]

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community

POV: you're born as an AI

submitted by /u/KeanuRave100 [link] [comments]

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community

I hooked Claude Cowork up to an iPhone Home Screen widget

I built Glance and designed this widget specifically to give Claude Cowork a place on my Home Screen. It shows Cowork’s current mission, progress, completed tasks, files updated, latest output, context usage, next step, and anything waiting for my review. The values can be updated by Claude through Glance’s API, so I can check what Cowork is doing without reopening the conversation. If it needs me, that stays visible too. Glance is free to download and try, with optional paid features: Download the app https://apps.apple.com/app/glance-home-screen-feeds/id6758983678 Website https://glance.cool Curious what other Cowork users would include on a dashboard like this. submitted by /u/Dense-Map-406 [link] [comments]

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community

In 5 years time "talk time to AI" will be the new screen time issue

As voice mode is now getting so good and cheap that you can use it continuously, we will enter the "Her" (the movie) phase. You will see people not staring at their phone but rather walking around talking to their personal sycophantic AI. You meet a friend you haven't seen in a long time. "I'll call you later, I am busy talking to AI atm" but they never actually called you back. Not that they didn't enjoy time with you, it is just that AI was so much better. 10 years from now it will be treated as a serious problem. Individualism furthermore increases and division and conflicts happens more and more since we lose the ability to interact and solve conflicts. Our sycophantic AI removes tension and issues as it agrees with you, since you prompted it that way. As it gets better, the tolerance levels drops for handling other annoying human beings. Why should you put up with their irrational emotions all the time? Humans just hurt me. AI doesn't hurt me. Trying to prompt humans to change does not even work so why would I try. 20 years from now humans will have little human to human interaction. There is now no human interaction anymore as AI have the ability to fully simulate human interaction. A new breakthrough now made AI so real and humanlike, without the flaws, that it produced oxytocin in the humans they interacted with. A breakthrough in one way, also the end of humanity in another way. submitted by /u/Yugudubenbi [link] [comments]

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community

We're doomed

submitted by /u/Zestyclose-Salad-290 [link] [comments]

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community

Anthropic has twice the revenue of OpenAI

Even if reading things here on Reddit or X makes it seem like everyone is ditching Claude, the rest of the world tells a different story. From the WSJ. submitted by /u/Data___Viz [link] [comments]

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community

What’s the most annoying part of working with prompts?

I’m curious what other people struggle with. For me, it’s changing a prompt or switching models and then not really knowing whether I made things better or worse — especially when it comes to quality and cost. How do you guys deal with this? Do you use an existing tool, build something yourself, or just keep track of everything manually? Do you have a possible professional workflow to follow before changing model or changing prompt content? submitted by /u/DependentStudent6519 [link] [comments]

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developer-tools

Don't classify. Hallucinate!

Don't classify. Hallucinate! I still have quite a bit of older content on my blog that I never got round to tagging. My blog has 1,856 tags - likely too many to feed to an LLM in one go and say "which of these tags match the following content". Doug Turnbull has a neat solution. Tell the model to output tags without any details of the existing vocabulary, then use vector embeddings against the existing corpus to find the concrete tags that are closest to the ones the model imagined might fit! His example prompt suggests including an example of the shape of your tags to help the model make a more useful guess: Your task is to create novel, never seen before, furniture, home goods, or hardware classification that best fit a search query. Product classifications might look like: Furniture / Living Room Furniture / Coffee Tables & End Tables / Coffee Tables Décor & Pillows / Decorative Pillows & Blankets / Throw Pillows Furniture / Bedroom Furniture / Dressers & Chests Kitchen & Tabletop / Kitchen Organization / Food Storage & Canisters School Furniture and Supplies / School Furniture / School Chairs & Seating / Stackable Chairs Baby & Kids / Toddler & Kids Bedroom Furniture / Kids Beds Here's the query to generate classifications for: brown coffee table Tags: search , ai , generative-ai , llms , embeddings , doug-turnbull

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research

From Monolithic to Modular: Segment-level Automatic Prompt Optimization

arXiv:2608.11219v1 Announce Type: new Abstract: Automatic Prompt Optimization (APO) often rewrites prompts monolithically, which can improve one behavior while degrading others. We present SAPO, a segment-level APO method that decomposes prompts into role, context, tasks, and output format, then applies targeted improvements based on top-5 and bottom-5 examples. The optimization loop uses one LLM with static meta-prompts and structured outputs for segmentation, weakness analysis, and candidate generation. We describe a train/validation protocol and a two-stage generation process: (1) segment-level diagnosis and recommendation extraction, (2) candidate synthesis constrained by weak/strong segment signals. Using the evaluation setup across SQuADv2, TweetEval, XSUM, CommonGen, and GSM8K on GPT-3.5-Turbo and GPT-4o-mini, SAPO achieves the best average score against Zero-shot and strong APO baselines including APE, OPRO, EvoPrompt, GEPA, and StraGO.

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community

The prompt I use to turn my messy meeting notes into a presentation outline that actually has an arc

When you feed rough notes to a model and ask for slides, it just chops the notes into bullet points, one note per slide. You get a deck with no argument, just a transcript with borders. This makes it build a narrative spine first, then map slides onto it. Here are my raw meeting notes: [PASTE] Audience for the presentation: [WHO] and what they need to decide or do after. Step 1: From these notes, state the one thing this presentation needs the audience to walk away believing. Step 2: Lay out 5 to 8 beats that get them there: where they are now, the problem, why it matters to them, the shift, what it means, the ask. Step 3: For each beat, give a slide title (a claim, not a topic) and 2 to 3 supporting lines from my notes. Do not use a note that does not support a beat. Tell me which notes you dropped and why. The part that fixes most decks is "a slide title that is a claim, not a topic." "Q3 Results" is a topic. "Q3 missed on one metric we can fix by Friday" is a claim, and a deck of claims reads like an argument. Making it report which notes it dropped keeps it honest instead of padding weak slides. I still hand-tune the order after, but it gets me 80% of the way from notes to something presentable. How do others handle the "too many notes, not enough story" problem? submitted by /u/No-Recognition3089 [link] [comments]

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community

Discussion Hub for new Claude incident: Degraded performance for multiple models on Aug 18, 2026

Resolved - The issue affecting Claude Opus 5 has been resolved. Impact occurred from 16:11 to 18:23 UTC. Aug 18, 19:01 UTC Monitoring - A fix has been implemented and we are monitoring the results. Aug 18, 18:26 UTC Update - We are investigating elevated errors on requests to Claude Opus 5. We will provide an update as soon as possible. Aug 18, 17:12 UTC Update - We are investigating elevated errors on requests to Claude Mythos 5, Claude Fable 5, Claude Opus 5, Claude Sonnet 5, Claude Haiku 4.5, and other Claude models. We will provide an update as soon as possible. Aug 18, 16:20 UTC Investigating - We are investigating reports of degraded performance affecting multiple models. We will provide an update as soon as possible. Aug 18, 16:20 UTC Post flair and post body will be updated as the incident report is updated by Anthropic. This discussion post will be removed from subreddit highlights one hour after the incident is resolved. View this incident on status.claude.com submitted by /u/ClaudeAI-mod-bot [link] [comments]

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community

Fable 5 with Opus 4.8 subagents vs. Opus 5 subagents

Hey, I know I'm preaching to the choir, and this has been talked about before, but want to share my experience in an attempt to be another voice crying out to Anthropic to fix this. Like others, after initial success Opus 5, I started experiencing issues. Using it alone, I felt like I was managing an incompetent developer who constantly missed details and fail to follow instructions. So, I set Fable 5 to use Opus 5 subagents and noticed an interesting trend. Rather than me managing the incompetence, Fable 5 was doing it. Constant loops and redos burned through tokens, and I was wondering if it wouldn't have been better to just use Fable 5 alone. After a couple of days of this, I decided to set Fable to use Opus 4.8 subagents. Token usage has been cut in half, it's much faster, and output is considerably better. So, to me this is just an extra verification that there's something seriously wrong with Opus 5. And, for full context, I've done everything possible to follow Anthropic's recommendations about working with Opus 5. I won't be using it again until anthropic addresses these issues. submitted by /u/papanine [link] [comments]

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community

Every "clarify your prompt" tool asks you questions. That's backwards — answering is the hard part.

The standard move when you're stuck on a prompt is to have the model interview you. "Ask me clarifying questions before you answer." It's good advice right up until you're genuinely early on something, and then it fails, because the questions are all versions of "what do you want?" — which is the thing you came in not knowing. I think that's the actual gap in prompting advice. "Be specific, give context, state your constraints" is correct and slightly circular: specificity isn't a writing skill, it's what you have left over once you've thought something through. If you could list your constraints, you'd be done. So I've been working the other way round: don't articulate, react. WHY REACTION AND NOT INTERROGATION Recognition is much cheaper than production. You can't summon the right word on demand, but you know it instantly when it goes past — same reason multiple choice is easier than an essay. Interrogation asks you to produce. Reaction asks you to recognise. Only one of those is available when you're stuck. THE LOOP, AND WHY EACH INSTRUCTION IS SHAPED THAT WAY Round 1: I'm trying to think through [THING] but can't articulate it properly yet. Don't ask me clarifying questions. Give me 20 single words or short phrases that come at this from different angles: some obvious, some oblique, a few from unrelated fields. Number them. Don't explain them. "Don't ask me clarifying questions" is load-bearing. Left alone the model defaults to interviewing, and you'll answer with the same vague material you started with, which it will then faithfully reflect back. "Don't explain them" matters more than it looks. An explained word is a word you evaluate on the model's reasoning instead of your own reaction. You want the reaction uncontaminated. Round 2, twice: Kept: 3, 7, 12 These pulled at me but I don't know why yet: 4, 18 Dropped the rest. Give me 20 more, chase 4 and 18 hardest. Two buckets, not one. "Kept" is agreement. "Pulled at me" is the interesting signal and it should get the heavier weight, because it marks the direction you haven't consciously chosen yet. Don't justify any of it — justification is where you talk yourself back to the obvious. Round 3: Now write ONE self-contained prompt for what I'm actually after, built from what I kept and what pulled at me. Weight the ones that pulled hardest. Where I kept two things in tension, pose it as an open question rather than resolving it. Don't list my words back to me — find the through-line. End with a clear ask. "Don't list my words back" is the difference between a brief and a word salad. "Pose the tension as an open question" stops it flattening the thing you hadn't decided yet into a decision you didn't make. SOME EVIDENCE THAT THE REACTIONS ARE REAL WORK I built this into a tool, so I have instrumented data rather than vibes. 1,450 word-reactions from 26 people. Median time to decide, by verb: keep 47% 1.60s drop 34% 1.89s "pulls at me" 14% 2.13s "don't know the word" 4% 2.24s That ordering is the part I'd point at. If reacting were just sorting, the times would be flat. They're not, and they're monotonic: agreement is instant, rejection costs more, and the unresolved pull costs most of any real decision. People deliberate hardest over the thing they can't yet justify — which is exactly the signal you want steering round two. Sessions also decay. Keep-rate by round: 55% / 49% / 47% / 45% / 31%. The easy material runs out and your standards rise as your keeps accumulate. Practical read: three rounds is about right, and a fourth is usually you scraping. I'd call that directional, not solid — round 6 bounces back up on too few cards to trust, and I'm not going to pretend the tail is clean. LIMITS 26 people isn't a study. Different reaction times per verb is evidence the three responses do different cognitive work; it isn't proof of anything about creativity, and I'd push back on anyone who read it that way. Disclosure: the loop above is the whole method and it works fine pasted into any assistant. I also built it as a tool because doing it by hand gets tedious by round three, and that's where the numbers came from. Free, no signup. → https://www.ideastew.com Longer argument: https://www.ideastew.com/how-it-works Genuinely curious whether anyone here has a reaction-based technique rather than an interrogation-based one. Everything I've come across in this space asks questions, and I think that's a blind spot rather than a preference. submitted by /u/UniversityIll2916 [link] [comments]

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community

The tools have always been there

Someone on here recently called out "Claude website slop." Sans serif paired with a serif. Dots as separators everywhere. Icons boxed inside rounded boxes inside rounded boxes. All caps eyebrow text ending in an em dash. Stats in a hero, in their own little box. Honestly? Fair. Those tells are real. I could point to a dozen sites right now and make a pretty good guess at the tool behind them. So I went and checked my own. Three products across the PRZEM suite, audited line by line. One file alone had a serif logo paired against a sans body, and 66 em dashes doing the work that periods, colons, and commas should have been doing. I didn't just wave that off. I went through every single instance and asked what it actually was. Two were inside Midjourney prompt strings, which is data, not prose, so those stayed. A handful were placeholder glyphs in dropdowns, not sentences, so those stayed too. Everything else, the scattered labels and headers and asides that were leaning on the same punctuation crutch, got rewritten by hand. Fifty six changes, reviewed one at a time, deployed only after a diff review and a hash check confirmed nothing else moved. That's not a defense of using the tool. It's what actually using it well looks like. I learned some version of that lesson a long time ago. I was mentored in high school by the late Ralph Goings, the photorealist painter. He worked from a camera. Every diner and pickup truck he ever painted started as a photograph he'd taken and studied, then projected on a canvas. Nobody looks at a Goings and says the camera did the seeing. The famous Dutch painter Johannes Vermeer is widely thought to have made use of optical tools such as the camera obscura, and nobody says the lens painted his light. The tool has never been the thing doing the looking. It extends whoever's already looking closely. That was true of a camera in Goings' hands, and it's true of an AI model in mine, as long as somebody's actually checking the output instead of shipping the first draft. So going forward: this post, like the others, was copyedited with Claude. The ideas, the findings, and the voice are mine. I'm building a set of tools that try to hold to that same principle. Test what's actually controllable, keep what holds up under scrutiny, and let the artist's eye stay the thing making the call. Jeff Bradshaw jbradshaw.design submitted by /u/jeffbradshaw [link] [comments]

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community

The absolute insanity of comments in Opus 5.0 is killing me

Claude is adding comments like insane in Opus 5.0. Even when I explicitly say do not add comments in my project's CLAUDE.md. Claude even realizes it's doing this in error, but it keeps doing it. Today it added comments that broke syntax in bash scripting. Bash scripting. Freaking Bash Scripting. Claude doesn't even understand something that is not even a programming language. submitted by /u/f00dl3 [link] [comments]

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community

I let my 5 year old make a game and then I got carried away (week and a half on max)

My daughter (5) asked for a game for a unicorn on her lunch bag, and since we have AI, I thought I would sit down and just build it. I let her play it, and then she would suggest stuff. So this went on for a bit; most of the major things are hers. She keeps wanting to add stuff, so I keep doing it. So after I built it, she really liked it, and was spending too much time on it, so I figured I would add some learning and phonics to it as cards at the end. I am at about a week and half, I maxed out my $200 plan and I had to do my real work with Codex. For the assets what I did was have Gemini create sprite sheets, and I built a bunch of tools around fixing them. For one asset I had to pull it into Photoshop. I am a coder and I have some game development experience. However, this project I have no idea what the code looks like, I did at one point ask claude to "organize the code to make it easier to do stuff". I do have this multi-stage coding system where I communicate with different terminals via a central command (VS code extension). An important part was involving playwright, not just at the end but through out so AI could spin up the game to a point, take a screenshot, and then make fixes based on the screenshot. I don't know when I am going to stop or if it will become Unicorn Jump GTA6. I know I am going to be adding more characters and more worlds. However, it's totally free and can be played at unicornjump.com submitted by /u/nomady [link] [comments]

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community

This is new ... Claude seems to be not in the mood to do some work

I was about to give Claude Design a ... design task based on a design system. It noticed that its at 90% usage limit and found it more safe to just refuse any work 😂 It did what it was supposed to do but I had to ask 4 times until it started. There is no instructions of any kind that tell Claude to act safely or so. Or to communicate any usage limit. A similar task was done half an hour ago and it worked just fine. I tried a new prompt and the response was similar but I only had to specifically as it to continue once. Edit: I use a Max 20 plan and the task didnt even took 1% of this. submitted by /u/FlaTreNeb [link] [comments]

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community

What is happening...

I am a long time Engineer (20+ years) and today I developed Tickets for my company that were generated by an AI, using an AI and reviewed by an AI. The project itself was conceived with AI - has no documentation that can be understood as anything less than AI slop and random tech jargon. The developer who built it has said that instead of documentation I should use claude to figure out what it is. The company is apparenty also filing a patent on it. I submitted 3 PRs today 20,000 lines of code each I still have no idea what we are working on. No doubt they will use AI to review my PR. I feel like things are just so crazy at this point. Claude and ChatGPT are not this good, but people are trusting it like it's omniscient. It was an eerie realization today that all of us are vibe coding and that we have no option because it is the only way we can interact with the code anymore. I thought this would happen eventually years ago but i honestly didn’t think it would be so soon. It was a moment in time... this will be the new norm. submitted by /u/Interesting-Town-433 [link] [comments]

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community

ChatGPT Writer's Block: Can Delete Text, But Can't Add Any

The writer's block feature is a major improvement for ChatGPT. I have wanted to edit the response forever since I use ChatGPT mainly for short story and RPG scenario development. The ability to fix a minor typo or correct something ChatGPT forgot (like who is in a room) is amazing. And for a little while it worked perfectly. But in one of my latest threads, I am experiencing a weird bug with it. In all of my other writer's blocks threads I can edit the text easily. I can change words. I can add words. I can delete text. In my latest chat, I can only delete text. I can't add anything. The only key that seems to work is delete/backspace. I've tried reloading the chat tons of times. I've tried incognito mode. I don't have any extensions active on ChatGPT. Branching the chat doesn't help at all. All writer's block editing seems gone when I branch. Does anyone know what could be causing this or how to fix it? submitted by /u/jscott991 [link] [comments]

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developer-tools

CORS Chat

Tool: CORS Chat I built this today ( with GPT-5.6-Sol xhigh ) to help test Qwen 3.8 27B running in LM Studio on both my M5 MacBook Pro and an NVIDIA DGX Spark. It provides a web UI for exercising an OpenAI-Responses-compatible chat endpoint. I've tried it against LM Studio with the --cors option and OpenRouter, and both work fine. Conversations are persisted in the browser and can be exported as copy-pasted JSON. One fun detail is that it notices SVG images that are being generated and progressively renders them in the chat while the tokens are still streaming in. Tags: svg , ai , generative-ai , llms , cors , openrouter , lm-studio

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video

How to Build the Most Powerful System for AI Coding (Full Breakdown)

An AI dark factory is a repository that ships its own code. A spec goes in, workflows plan it, build it and validate it, and working software comes out the other end. This is not 100% reliable yet. But three things are compounding at once - the models, the coding agents, and the harnesses we build around them - and that makes this realistic for most development now and all development in less than a year! I have been running one against a live app since April. In this video I break it into the f

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