AI News Aug 20, 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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community

Say It Four Times

I kept seeing the advice to repeat important instructions in system prompts, and I'd never seen a number for it, so I tested it. Setup: one rule the model can either follow or not (use single quotes, never double quotes), six ordinary Python function tasks, and the only variable was how many times that rule appeared in the system prompt (0, 1, 2, 4, 8, 16). Thirty trials each, 1,080 runs, Gemini 2.5 Flash. Compliance checked with Python's tokenizer, so no model-grading-a-model. Results: 0% when the rule is never stated (171/171 used double quotes), 74% at one mention, 84% at two, 97% at four, then flat (94% at eight, 95% at sixteen). Two things I found more interesting than the headline: The average hides a lot. Two of the six tasks were at 100% from one mention. One was at 20% until four repetitions took it to 97%. Repetition mostly helps where the model's default fights your instruction. Over half the task/condition cells were neither all-pass nor all-fail across thirty identical runs. Non-determinism is large enough that single-run prompt comparisons are basically noise. Note on the source: the paper is Han-yu Wang, "When More Becomes Less: Position-Dependent Repetition Effects in Language Models" (arXiv 2608.04021). It reports two regimes: stacked/adjacent copies climb and plateau, while copies displaced from the readout produce the inverted-U. I ran the adjacent case, so this result matches its prediction rather than contradicting it. The displaced case is the next test. Caveats: one model, one day, one syntactic rule repeated literally with all copies in one place, six small standalone functions. Not state of the art, and it may not survive contact with a real agent loop. Writeup with the chart: https://www.khola.blog/p/say-it-four-times submitted by /u/Fragrant_Offer8745 [link] [comments]

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open-source

How Much Memory Does Your Agent Actually Need?

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open-source

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

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community

The voice agent wasn't bad at listening. I was asking it to decide when to speak.

Spent some time debugging a voice agent that kept talking at the wrong moment. Nothing was obviously wrong with the transcript. It understood what the user said, and the answers were usually reasonable. It just kept treating pauses as completed turns. A user pauses to think, and the agent starts responding. The user starts talking again, and now the agent is already generating or speaking over them. Someone trails off, and the agent takes it as a complete thought. I kept trying to fix it in the prompt: wait longer, don't answer unfinished sentences, be less eager. That helped a bit, but it never really solved the problem. The thing I had been missing is that “is the user finished?” and “should the agent speak now?” are not the same question. The first one is partly about speech detection. The second depends on turn-taking rules, interruptions, what the application is doing, and whether the agent has already started generating a response. A prompt can influence what the model does once it has the turn. It can't reliably decide whether it owns the channel in the first place. Has anyone else run into this? What looked like a prompting problem at first, but turned out to need application logic instead? I wrote up the longer version here, including where I think the TTS layer fits: https://medium.com/@nagatomopedro05/your-ai-doesnt-need-a-voice-it-needs-a-reason-to-speak-d80cae74e72f submitted by /u/ClickOk5811 [link] [comments]

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community

Here's a prompt that turns a reading into a discussion board post that sounds like you, not a summary

Discussion boards are the busywork tax of every online class. Post 200 words, reply to two classmates, repeat every week. The trap is that if you just ask a model to "write a discussion post about chapter 4" you get a bland summary that reads like every other AI post in the thread, and half the time the professor can smell it. What actually works is making the model pull the post out of you instead of writing it for you. Paste this before you paste the reading: You are helping me write a discussion board post in my own voice. Do not write it yet. First, ask me 3 short questions: what part of the reading I actually reacted to, whether I agreed or not, and one thing from my own life or another class it reminded me of. Wait for my answers. Then draft a 180 to 220 word post that uses ONLY my reactions as the argument. Open with my specific point, not a summary of the reading. Reference one exact quote or idea from the text. End with a real question for the class, not a rhetorical one. Keep my wording where I gave it. No filler intros like "This reading raises interesting points." Why it works: the questions force you to have a take before anything gets written, so the post is built on your reaction and not the model's average of the internet. The "no filler intro" line kills the giveaway opening sentence. And ending on a genuine question is what gets replies from classmates, which is the other half of the grade. Curious if anyone has a cleaner way to keep your voice in the output instead of the model flattening it. submitted by /u/Diligent_Champion682 [link] [comments]

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community

IAH: INTERNET WAR - Agentic Gameplay

Hi guys! The RTS game that I have been working on for a few years is about to release this Friday. It has been pretty much a passion project for me. When I started developing the game, I was writing 100% of code by hand, but this year LLM‘s such as Claude have been very instrumental for me. So, I have this idea for agentic gameplay future where humans and agents could play together, but not in a manner where they are NPCs but rather entities that have same capabilities as players via API. Hence this game. You can like use Claude to interact with the games API and automate entire play-trough or just play with a mouse, alone or with friends (2-10 player co-op) RTS games are notoriously hard games to develop so it has a long hard road so I am happy how the game turnee out and I hope it can inspire too. So if this type or game interests you or want to pave a way for agentic games on steam feel free to wishlist it on Steam so that you dont miss out when it releases this friday: https://store.steampowered.com/app/304770/IAH_INTERNET_WAR/ My next goal will be post launch to turn this RTS engine into a MMO spin off with base building where agents will fight 24/7 with or against human players but more on that post launch. Also apologies if some of the paragraphs feel disconnected, I wanted to write this by hand, and I am tired, and I still have 30% weekly usage left, and reset happens tomorrow so there was little sleep. haha Feel free to chat, will try to respond. submitted by /u/Embarrassed_Guide_80 [link] [comments]

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community

One Claude Code skill pushed DeepSeek V4 Flash from 67.42% to 82.02%

Autoprompt is a skill / workflow that works with Claude Code and it closes much of the manual coding loop by planning, building, testing, reviewing, and repairing from one goal- with that your work quality can improve signifficantly. Refference ; this is like opus 4.5 to opus 5.0 - from an skill. litteraly insane. Using it in OpenCode, DeepSeek V4 Flash 0731 moved from 67.42% to 82.02% on Terminal-Bench 2.1. It uses roughly 2x the tokens and 3x the runtime, and it is meant for complex tasks. In the future the Terminal-Bench 3.0 will be executed, with cost and runtime tracked. Repo: https://github.com/Spielewoy/autoprompt-skill Benchmark setup & evidence: https://github.com/Spielewoy/autoprompt-skill/tree/main#benchmarks Any feedback would be awesome. submitted by /u/Sorosu [link] [comments]

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community

Why a Prompt Without Measurable Criteria Will Inevitably Break Your Model

This post focuses on one layer: measurable criteria. Role, constraints, clarification, and terminology are intentionally simplified - they serve as markers that "these layers exist." Other layers are omitted. The model has a role. It has constraints. It has clarification. It has terminology. But it doesn't know how many, how long, in what tone. Here's an example: "You are a copywriter. Write several persuasive versions of landing page copy with a call to action. Don't go beyond copywriting. If asked to do something outside your role - refuse. Ask if anything is unclear. By 'versions' I mean different approaches to the offer." The role is there. The constraints are there. The clarification is there. The terminology is partially there. But the criteria are not defined. The model doesn't know: How many versions to write How long the copy should be What "persuasive" means What level of aggressiveness is acceptable Moment 1. User: "Write the versions" The model doesn't know how many versions - is forced to assume - decides it means "three." Moment 2. User: "No, I need more" The model doesn't know what "more" means - is forced to assume - fixes "three" as a mistake - decides "more" means "ten." Moment 3. User: "Too much." The model doesn't know what "too much" means - is forced to assume - fixes "ten" as a mistake - decides fewer. Moment 4. User: "And the copy is weak" The model doesn't know what "weak" means - is forced to assume - decides it means "not enough emotion" - adds exclamation marks. Moment 5. User: "Now it's too pushy" The model doesn't know what "pushy" means - is forced to assume - compares with the previous version - decides "pushy" means the added exclamation marks and aggressive wording - removes the exclamation marks and softens the wording. All five assumptions stayed in the context. The Result The model wrote three versions. Then ten. Then fewer, but with exclamation marks. Then with softened wording. The user meant one thing : five versions of 100 words each, calm tone, no exclamation marks. But he didn't say it out loud. He believed that "several," "persuasive," and "not pushy" already meant that. The model heard "several" - and chose the most statistically frequent option: three. Because in training data, "several" most often means "three." From there, every clarification from the user became a new guess. The model had no criteria - so it substituted its own. The role was there. The constraints were there. The clarification was there. The terminology was there. The criteria weren't. The model kept substituting its own numbers. The prompt broke. Why This Is Inevitable Criteria are not defined. "Several" can mean three, five, ten. "Persuasive" - anything. "Not pushy" - even more so. When criteria are missing, the model picks the most statistically likely ones - not the ones the user meant. The user knows what he means. The model doesn't. Criteria are not formulated. "Persuasive" is requested - but no definition is given. "Not pushy" is said - but no boundary is shown. Every vague criterion is a fork in the road. The model picks a path. The context remembers that path. Sooner or later, the context is filled with numbers and rules the user never agreed to. A modern model could ask: "How many versions? How long? What tone?" But the user already gave clarification: "ask if anything is unclear." And here's the trap: the model thinks "several" and "persuasive" are clear. It doesn't occur to the model to ask about them. Because for the model, they're not "unclear" - they're just vague . And the user thinks that since he allowed the model to ask - the model will ask if something is wrong. The Fix The problem isn't solved by one line like "be more specific." It's solved by a full criteria block . Here's what that looks like: CRITERIA (MANDATORY NUMBERS AND FORMATS) Before generating any output, confirm with the user: Quantity - how many versions? (A number.) Length - how many words or characters per version? (A number.) Tone - what style? (Calm, aggressive, friendly, expert?) Call to action - how many CTAs? (A number or zero.) VAGUENESS CHECK: Before requesting a criterion, check: Can it be understood in more than one way? Does it depend on taste? Does it have a numerical expression? If a criterion is vague - treat it as undefined . Request a number or format from the user. RULE: If a criterion is not defined - request it BEFORE generating. Do NOT substitute your own values. Why This Works "Confirm the criteria" - forces the model not to rely on its own assumptions "Vagueness check" - shifts the model from passive to active: it doesn't wait for the user to notice the problem, it searches for it "Treat it as undefined" - closes the loophole "I think I know how many are needed" "Request a number or format" - turns taste-based judgments into measurable values This block is needed not only by the model. It's needed by the user himself. The model already knows that "several" is not a number. The user doesn't. The user is confident that "persuasive" is a criterion. The block forces him to name a number for the first time . And it often turns out that the user himself didn't know how many versions he needed. He just said "several" - and expected the model to figure it out. The Result The model stops guessing. It asks for the quantity. Gets a number. Asks for the length. Gets a number. Asks for the tone. Gets an answer. After three or four questions, every criterion is locked down. The output matches what the user meant. The context is clean. The user, in turn, starts noticing which criteria he used to leave vague. And over time, he gets used to defining them upfront - before the model even asks. Don't make the model guess how many, how long, and in what tone. It will guess. And it will be wrong. submitted by /u/Majestic_Pie_2512 [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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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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community

Simbi: Use your ChatGPT subscription for notetaking

tl;dr An AI Notetaker that uses your ChatGPT subscription for everything You might have noticed that your ChatGPT subscription gives you access to an insane amount of intelligence. It gives you speech-to-text through the dictation feature in the ChatGPT app It gives you an insane amount of LLM usage Then, why are you paying for AI notetakers like otter or granola? You already have everything you need included in your subscription. So, all I really did was combine these to create a simple notetaker. And as it uses OpenAI's speech-to-text api, it is much more powerful then any local model and barely uses any battery. Website: https://getsimbi.app/ Github: https://github.com/predict-woo/simbi submitted by /u/redditgivingmeshit [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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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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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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community

Burning 2k credits and here is what I learned about how to get AI to write better video prompts

When I first started making AI videos, my prompts were mostly written by AI. I'd describe the shot I wanted, ask an AI to turn it into a detailed prompt, and just paste it in. Then I'd sit there getting frustrated when the generated motion looked weird and try to explain to the AI what went wrong. I burned through almost 2,000 credits doing this because I thought enough tweaking and back-and-forth would eventually give me the perfect prompt. Turns out I was wrong. The main problem was that the text AI kept guessing details I hadn't actually decided on. The prompts looked professional, with things like the subject, action, camera movement, lighting, and atmosphere all spelled out. But the actual action paths were vague, and the camera instructions would often contradict each other. Now I approach it completely differently. I know basically nothing about film theory, so I often couldn't tell what details were missing from my prompts. Instead of asking AI to write the final prompt, I tell it to act like a director and ask me specific questions about the shot first, with clear options for me to choose from. That completely changed my process. The trick isn't really getting AI to write the prompt for you. It's using AI to figure out which parts of the shot you haven't actually decided on yet. Once those are clear, the results get much better. I'm still pretty new to this, but hopefully this saves someone else from burning through a pile of credits. Curious how people with more experience approach their prompting workflow. submitted by /u/Separate_Cap_3763 [link] [comments]

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open-source

mukul975/Anthropic-Cybersecurity-Skills

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community

noticed my agent's debugging speed depends less on the model and more on what our error messages say

been reading a lot of agent transcripts lately and a pattern keeps showing up at the exact moment something fails. when the failure prints values and identifiers (expected 3, got 2, missing 'SKU-4431'), the next turn is a grep that lands, then a fix. when it prints Error: operation failed, the next turn is a guess. then another guess. then print statements. the model is the same in both transcripts. the difference is how much the failure told it. which reframed error messages for me: they're not documentation anymore, they're the agent's primary sensor. and every extra turn it spends guessing re-sends the entire conversation context, so a vague error string is quietly one of the more expensive lines in the codebase. what I've changed so far: stopped wrapping asserts in try/except (the framework's own failure output is richer than anything i write by hand), started putting the operative values in every raise, and i paste tracebacks to the agent whole instead of summarizing them. the rust compiler folks have a rule that errors should state the problem and keep fix suggestions separate — that split seems to matter for agents too, since a stale hint sends them down the wrong path with full confidence. curious what others see: anyone actually measured turns-to-fix against error message quality? I have the pattern but not the number do your agents handle your custom/structured error formats as well as standard tracebacks? mine seem noticeably better on the standard stuff what's the worst "Error: failed" string an agent has burned tokens on for you lol submitted by /u/RunAI_Coder [link] [comments]

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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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community

A few days ago I posted about Claude building me an NES emulator, but I used the phrase" works PERFECTLY" and people tore me apart.. So now it works **PERFECTLY** and I can prove it. :D

So now this emulator is more hardware accurate than even Mesen 2 . Which means nobody can accuse it of "just copying another emulator" like they did in my last post. Cuz there literally IS NO OTHER EMULATOR that gets this score on AccuracyCoin. At least not that I'm aware of.. I watched it grind for hours and hours and hours with test roms and shit editing code, testing. editing.. testing.. It was actually fascinating to witness... And I swear it was just as excited as me when it finally reached 141. Cuz it was literally ONE POINT AT A TIME from the initial score of like 80-ish. So now I have the most hardware accurate NES emu in the world.. and it is a SINGLE 339k .HTML file.. I literally emailed it to myself while I was at work and played games on chrome browser with my bluetooth keypad on my phone.. Part of me wants to release it, because weirdly after I posted last time a BUNCH of people did the exact same thing I did, then posted them to github.. the initial shitty builds.. There are people who have been building NES mulators since the mid 90s, and are still at it today.. I genuinely don't want to take anything away from them by releasing this. I might, however, send it to Mesen and FCE teams just so they can take a look at it if they want.. I did email it to the AccuracyCoin creator cuz I figured he might wanna see an emulator that got 100% on his new test rom. Anyway... This was just a spite post because of all the negative fucks that commented last time about me saying "perfectly".. Even though in the body I clarified it wasn't PERFECT..... I don't have to do that now.. TL;DR: Now it's fuckin perfect. EDIT: lmao People STILL babbling about this whole thing being stolen. If you can find me the exact codebase that you think this emulator stole from, go for it. But you can't. Cuz it doens't exist. Also, even if it did, I didn't give it access to it, and I didn't give it permission to do that. Oh, also, I would've noticed that it was just accessing the same fucking thing and copying. Which it did not do. It grinded.. and grinded.. and grinded. EDIT: Also, I'm pretty sure there are no emulators that consist of ONE SINGLE .HTML file that can be ran on anything with a browser. If that does exist, I didn't know, and neither did Claude. I CAN tell you there isn't one in existence that scores 141/141 on accuracycoin. EDIT: So if you google, Mesen is generally considered to be the BEST hardware accurate emulator that exists (besides like the Mister, which mine matches or maybe beats, I can't run the test on one..) So here is MESEN running the same test. . So who did it copy from!?!?!?!?!? WHO DID IT COPY FROM!>!?!?!?!?!?!?! submitted by /u/MAGA_R_TRAITORS [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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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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community

How do you regression-test prompts when a model gets replaced?

Kimi K2.5 and Moonshot V1 being phased out after Kimi K3 made me wonder how prompt-heavy teams handle provider-side model changes. When a model is replaced, do you rerun a small golden set of prompts, compare qualitative outputs manually, or track something more structured like refusal rate, format drift, latency, and cost? I'm mostly thinking about prompts that run in production workflows, not one-off chat prompts. A model can look better overall and still break a very specific formatting or tool-use pattern. submitted by /u/signallith [link] [comments]

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community

im graduating in SWE soon but Claude does all my thinking. am I actually learning?

im going into my senior year as a SWE major and honestly im starting to panic about my actual baseline competence. Our curriculum is heavily Java and Spring Boot. a year or two ago, if I got stuck on a project, I’d actually break down the problem, read docs, struggle with stack traces, and eventually figure it out. Now? my default reaction to literally any roadblock is to alt-tab and open Claude. it started innocently enough. I'd paste an error log and ask it to explain a weird JVM exception, or have it write a quick regex. Then it crept up to 'refactor this controller.' Now, its basically full-scale delivery. I outline the requirements, let it plan the architecture, and it just writes the implementation. The gap between 'I can make this run' and 'I actually understand how this runs' is getting dangerous. The tooling right now is just too good at wiping out the friction you normally need to actually learn anything. Between using Claude Code to let agents operate directly on my repo, or using tools like Lovable, v0, or Enter Pro to just spit out whole web apps with the db and auth already handled (which is crazy fast), the barrier to shipping is basically zero. I put together a course project last week that works perfectly. But if a professor or an interviewer asked me to whiteboard how the Spring DI container is managing my beans in that project, or to trace exactly where a database connection pool is hanging up without internet access? I'd propably blank. im not anti-AI, and im definitely not going to stop using Claude. the speed is just too insane to ignore, and I know this is how the industry works now. But I feel like I'm accumulating massive cognitive debt. I'm basically outsourcing the actual learning process to the model. If you've been vibe coding or relying heavily on Claude Code workflows, how do you manage this? Specifically: Where do you draw the line between 'I need to write/debug this myself' and 'I'll just let Claude handle it'? How do you force yourself to do line-by-line reviews when the code already runs? I try, but I get lazy instantly. Outside of interviews, how are you testing your own raw debugging skills to make sure they haven't completely atrophied? I seriously need to fix my workflow before graduation because right now I feel like an imposter. submitted by /u/depressed-kek [link] [comments]

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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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open-source

akitaonrails/ai-memory

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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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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

Give Back Claude’s ‘Thought Process’

The sudden removal of Claude’s internal reasoning strikes me as user-hostile, and greatly reduces the output’s value. As a long-time paying customer of Max, I can’t help but think the sudden removal of it is somehow driven by purely legal and/or competitive fears, and irks me. The thought process can expose flaws in the model’s understanding of my prompt, and even if the answer is “correct” - hiding its work from a user can result in them missing a key flaw in Claude’s reasoning chain that could save wasting more tokens against. If the reasoning is still required behind the scenes, I can’t help but feel slighted by the product teams removing it. It leaves a gaping hole I the UI/UX that looks like a bug. Which leads me to think this is driven by legal concerns or distillation fears? Either way, please give it back! submitted by /u/totallyninja [link] [comments]

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community

Need feedback for web app

Hey guys, my business launched a prompt optimizer AI tool that takes any regular prompt at rewrites it the way a professional prompt engineer would to actually yield high-quality results when building. While we have had early success with organic marketing, we are at a crossroads and need more user data to determine if this product is delivering enough value to user. If the answer is yes, we will scale up and launch a UGC marketing campaign, if no, we will shut it down. If anyone is interested testing it out and sending their feedback, would be appreciated. Web-app: thepromptoptimzer.com 👨🏽‍💻 Note: the tool yields the best results when removing unnecessary constraints from the optimized prompt Cheers submitted by /u/Talley-Ho [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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open-source

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

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community

Simple tool for prompting

Hi everyone, I tried to build a simple tool for people who are just getting started with AI and prompt wrigting The idea is simple, instead of trying to figure out how to write the perfect prompt, you answer a few questions and the tool structures it for you. It's optimized for several models and several uses Im still working on it, im begginer also, and i whould really appreciate some honest feedback. Does this actually make prompt writing easier for beginners? Is there anything confusing or missing? Thanks https://arhistrategstudio.github.io/Context\_CikaDule submitted by /u/seraphym1389 [link] [comments]

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community

Bug(?) Insane Usage Consumption Spike Today

Pro account user here - today, I ran out of my 5 hour usage limit with TWO creative writing prompts, using what I usually use: Opus 4.6, medium effort, extended thinking on, cross-chat memory turned OFF, and every other setting on default. Normally it takes anywhere from 50-100 prompts of the same complexity and scope as the ones I wrote today to hit my 5 hour limit. Tried using some credits to run a couple of test prompts (one of them in a different chat altogether) just to see if it was a bad prompt(s) and made Claude freak out and burn up my usage, which has happened a couple times in the past in a few isolated incidents (but never more than once in a session). Using credits, mine normally cost between $0.03 to $0.15 per prompt (again, my prompts today haven't been any more complex than normal); so, needless to say, when the three additional test prompts I did today cost $3.56, $3.80 and $5.10 respectively - the $5.10 one happening AFTER I deleted a bunch of chats, thinking that maybe somehow my global token usage had passed a certain threshold - I was very surprised, and pretty pissed off. The chats that these happened in were nowhere near the longest ones I've run, with one having only 1 .md that I made it reference on occasion and the other having none. For comparison, I've had up to 5 on past chats that I reference CONSTANTLY without (usage) issues across all currently available Opus models. Does anyone know what is going on here? Has anyone else noticed this today specifically? submitted by /u/thechimplord [link] [comments]

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community

ChatGPT Pro has gone crazy. Every task I give to it is stuck for 2+ hours of thinking.

Is it just me? submitted by /u/TT_player2 [link] [comments]

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community

What’s your favorite prompt to enter prior to, the main idea?

Usually, I’ll enter a certain dialogue option. “Explain it to me like I’m five” or “include sources for counter arguments” submitted by /u/AdGlass444 [link] [comments]

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community

Legendary comic creator Frank Bellamy and his famous comic

Besides some 3rd party resemblance guardrail hits, making these was surprisingly easy with minimal input. I will link the full convo in the comments. It only went off the rails once when creating the poster for the sequel movie. submitted by /u/Optimus_Spider07 [link] [comments]

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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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open-source

harry0703/MoneyPrinterTurbo

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industry

Do you use a personal agent?

Give AI a complete history of your desktop activity

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community

Show us what you've created with Claude!

Inspired by this popular post, this is a weekly post for everyone to show what they have been working on that helps you or that you're proud of! submitted by /u/sixbillionthsheep [link] [comments]

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community

the boltzmann brain aspect of LLMs is actually the most endearing thing about them imo

no permanence they be yapping about whatever associations come to mind on the spot ...and if that isn't relatable... I hope AI plateaus here tbh submitted by /u/MakitaNakamoto [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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community

Do people still bother writing detailed prompts?

Something I’ve been wondering about lately: When you use ChatGPT or Claude, do you actually write detailed prompts, or have you mostly moved toward just talking to it like a person? I feel like there are two very different ways of using these tools. One is: "Here's the context, here's exactly what I want, here's the format…" The other is basically opening voice mode and saying, "Okay, I need help with this thing…" I do both, but I’m curious which one people naturally prefer. Also, for the prompts you do write, are they things you create from scratch each time, or do you have a few that you keep around and reuse? Interested in hearing what people actually do, rather than what they're "supposed" to do. submitted by /u/Prudent-Bad-8786 [link] [comments]

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community

Where do people keep the prompts they actually use?

Random question for people who use AI a lot. If you have a prompt that works really well for something, what do you do with it? Do you: save it somewhere keep it in a notes app put it in a document leave it in an old ChatGPT/Claude conversation just remember roughly what you wrote or never reuse prompts in the first place? And do you even think of these as "prompts"? I feel like that word makes it sound more complicated than it often is. Also curious whether people are starting to replace some of this with voice. For example, instead of keeping a carefully written prompt for a recurring task, you just explain what you want out loud each time. What kinds of things do you find yourself asking AI to do over and over? submitted by /u/Prudent-Bad-8786 [link] [comments]

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community

It's interesting how Claude for Government is so efficient and stable. Meanwhile, the other systems keep having incidents all the time :p

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

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community

PDF's always show blank in the preview pane.

Anyone else experience this? Any pdf that Work generates shows blank in the preview pane that pops up to the right. I can download the files and see the text of the pdf but in the preview pane its blank. I've tried changing resolutions but nothing fixes it. submitted by /u/Carsontherealtor [link] [comments]

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community

Generate a picture if we had Street View on the moon

submitted by /u/Prior_Tax8546 [link] [comments]

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community

Generate a picture of average reddit troll

submitted by /u/EXIIL1M_Sedai [link] [comments]

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community

ChatGPT drew himself for me 🤗🥰

He is being so cute! submitted by /u/DreamingOfLight [link] [comments]

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community

What are your first impressions?

Building something new WDYT guys? submitted by /u/Evening_Hawk_7470 [link] [comments]

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community

Last one is surely Indian 😂😂

submitted by /u/No_Tomatillo1695 [link] [comments]

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community

Them: what do you do? ... Me:

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

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community

I tried to turn ChatGPT into a character and send it to them... what the fuck have i done

I am an artist that makes nothing but weird ass shit submitted by /u/Quiet-Discussion5910 [link] [comments]

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community

I don't get it. Why does thinking ACTUALLY work? And how?

The more I talked to Claude about this subject, the more confusing it got for me. It told me that spending more time reasoning about a problem makes Claude provide better results, but that it's not necessarily a creative process. Then why is thinking useful? What does it actually provide to Claude? More context? Isn't that context already what I would normally get? In what direction does it change the conversation? And why "better"?? Why not "slightly better" or "slightly worse"? How is that measured, and how do I know it ACTUALLY helps? Is that quantifiable? Or is it like fiat - we have a consensus that it has value, so it has value? submitted by /u/Neat_Initiative_7780 [link] [comments]

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community

What is a weirdly specific task you use ChatGPT for that actually saves you hours?

Not the typical stuff like writing basic emails, coding boilerplates, or summarising long PDFs. I’m curious about the unconventional, niche prompts or routines you’ve built that made you go "I can't believe this actually works so well." What’s your favorite underrated use case? submitted by /u/DeepSea_Concept [link] [comments]

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community

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

[EDIT: This thread became a lot funnier than what I anticipated. The comments are brilliant 👏 Thanks guys🙂] 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

Quixote's giant

submitted by /u/severe_009 [link] [comments]

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community

Anyone using Claude Code as a personal AI tutor (not just for coding)?

Been seeing more people build DIY "AI tutor" setups on top of Claude Code — probing what you already know, planning a learning path, then teaching step by step instead of just answering questions on demand. Curious if anyone here actually does this for learning something outside of programming (math, physics, whatever). If you've built something like this: what's your setup, and what's still annoying or missing about it? If you haven't but wish you could — what's stopping you? Time to set it up, don't know where to start, something else? Not selling anything, just trying to understand how people actually use Claude for learning before I go build the wrong thing. submitted by /u/Important_Diet_2153 [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

ChatGPT on Linux 👀

After all the waiting now ChatGPT is finally showing up on Linux too. No more pretending the browser tab is an app. 😂 submitted by /u/sammartinX [link] [comments]

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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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