OpenHands

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

YouTube Transcript Getter Extension - For Obsidian Karpathy Wiki

Helloooo there, I recently created a Karpathy style LLM managed Obsidian Wiki to try to capture all of the big themes and developments in AI and AI Engineering. Some of the best sources for this kind of thing are YouTube videos. I built a couple of MCPs using APIs etc, but they didn't work out so well for pulling transcripts. So I went about it in a different way, I built a lightweight Chrome Extension which I use to export transcripts and video details to markdown format. It has a few modes, one which is the big button mode, which shows an overlay and you click a button, and the transcript is pulled (along with other video details). The other is an autodownload mode, which autotriggers on landing on a video page. Again I tend to use these as I am watching a video and find it interesting, but it also does open up the possibility for a browser use agent to land on pages and either... Click the big old button to get the transcript Or simply trigger an auto-download This can be done with simple skills or even scheduled tasks potentially. Anyways I find the whole thing of custom built chrome extensions for browser use agents pretty interesting - it kind of gives them a helping hand if you want something automated on a page (rather than them clicking around - and risk them getting stuck). This is an experimental extension, so be considerate in how you use it, as I say I don't use it for any kind of mass activity - mainly as a simple helper when I am watching something interesting. The repo: https://github.com/smartaces/yt-transcript-chrome-extension As I say I find it very helpful for documenting useful video content etc for my wiki! submitted by /u/Smartaces [link] [comments]

Sam Altman personally replied to an open source dev tool project — and most people missed it

Was digging through Sam Altman's replies and found this — back in May he responded directly to GitLawb, the team building git infrastructure for AI agents. They'd tweeted that if he replied, they'd make GPT-5.5 the default model and Codex the default provider for their platform (which even then had 25.7K GitHub stars, 8.3K forks, 116K downloads). Altman replied "cool thanks!" — the post has 369K views now. The OpenAI CEO doesn't hand out replies. His timeline is almost entirely researchers and AI-lab people. So when he stops to acknowledge a project by name, it's worth noticing what they're building. Since then the same team shipped Zero — an open source terminal coding agent (single Go binary, 24+ model providers, MIT licensed, no telemetry) that's pulled ~800 stars in its first two days. submitted by /u/amu4biz [link] [comments]

Is OpenAI doing shady usage shit right now? I have Pro20x and it feels like Plus

I have Pro 20x, no issues with usage for months. Go to bed with ~80% and wake up my usage is set to 0%. Now my usage with GPT 5.5 feels like when I had Plus. Any other Pro20x users out there getting same experience? EDIT: To all those saying I left long-running tasks or whatever else, this not the case. I'm a long time user and seeing a handful of tasks draining my hourly/weekly usage as if I had Plus is VERY noticeable. I don't need to be benchmarking because I can also see in my usage dashboard and compare it that way as well. Something is up with their quota. EDIT 2: Over the past few months, my usage patterns have been relatively consistent. However, beginning around July 1, my dashboard changed dramatically: Exec usage suddenly began consuming nearly my entire daily allocation. Turns increased from roughly 100–150/day to over 800 GPT-5.5 turns/day. At the same time, Skills Used did not increase proportionally—in fact, they were lower than on some earlier days. Date GPT-5.5 Turns Skills Used Exec Jun 20 111 High usage period Normal Jul 1 779 73 Nearly maxed EDIT 3: SOLVED -- There are open github issues on Codex indicating this is potentially a product bug. To all helpful people, thank you. To the rest, get help. submitted by /u/aenemacanal [link] [comments]

Every few weeks one of the big labs ships the thing I was about to build. How are you deciding what to build now?

Trying to ship a product while OpenAI/Anthropic/Google ship features every couple weeks that flatten whole projects. I had an analytics feature scoped where customers build their own dashboards. Killed it, they'll just ask an AI for a dashboard in seconds. So now I only build stuff tied to my own data and context, the bit nobody can hand me, and rent or wait for the rest. How are you deciding what's still worth building yourself vs just waiting for it to ship? submitted by /u/Alternative_Letter72 [link] [comments]

i timed my monthly investor update and ~80% of it was just gathering inputs

Did this out of curiosity last month. The actual writing was maybe 15 minutes. the other two hours was me being the courier, pulling granola call notes, last month's metrics out of a sheet, and the three open gmail threads with investors into one place so i could even start. For years i assumed the writing was the bottleneck. it wasn't, it was the assembly. so i handed the gather step to one of those desktop ai agents that can read granola, gmail and a metrics doc inside the same task instead of me tabbing between them. it came back about 80% drafted and i edited the rest. the draft quality wasn't the surprise. Not opening six tabs to rebuild the month was. if you write a recurring update, where does your time actually go, the thinking or the input-gathering? mine was almost all gathering and i had it backwards the whole time. written with ai submitted by /u/Deep_Ad1959 [link] [comments]

People keep talking about Fable 5 ban and now GPT 5.6 not being released to masses, but do you guys think if AGI is ever reached, any government would allow unrestricted access to everyone?

I truly don't think LLMs can ever reach AGI given how they fundamentally work and we don't have any paradigm shift in the tech yet which can pave the way for true AGI, but I digress. Hypothetically if AGI is ever reached, do you guys think any government would allow unrestricted access to everyone? At that point it would no longer be just about writing code or getting answers to anything by prompting. We are talking about systems that could impact geopolitics, economies, cybersecurity, warfare, intellectual property and entire industries. Also, even as LLMs are getting powerful, first Fable 5, now GPT 5.6 and the game is heading in a direction of tighter control, blocking foreign access and highly regulating domestic access to make sure that select FEW national companies and national security stay on top and not dethroned by outsiders and foreign bodies. Anthropic freaked out because Alibaba had 25k accounts distilling fable possibly to build their own models. These can lead to foreign countries ending up with more powerful systems with the help of American models. Given where it is headed with how this is playing out, maybe the longterm answer is for countries/local industries to develop their own models and progress instead of being at the mercy of US government/US companies because companies who have access to american frontier models will have unfair advantage over those who don't have it and US (and countries in general) is well within their rights to decide which path they want to take. This is exactly like military/nuclear race and countries developing their own military capabilities so that they don't have to rely on someone else to protect them and exert their dominance. Also, opensource models won't have the money/research capabilities to match companies like OpenAI (with government backing) and Anthropic (who used to have gov. backing). Looks like this would be the same story from here on with every new model release from Antrhopic or OpenAI where it won't be released to masses and rolled out internally within government approved authorities. All we would get with every release are breadcrumbs highlighting how POWERFUL and dangerous those models are without ever getting our hands on them. And/if the general public ever get those models, those would be HEAVILY nerfed anyways so we won't get their full capabilities. submitted by /u/simple_explorer1 [link] [comments]

We are officially in Minority Report territory

A 36-year-old man was preventively arrested in Espírito Santo after conversations held with an artificial intelligence revealed a plan that included killing his own son and promoting attacks against schools, churches, and public authorities. The case, which mobilized security forces at different levels, originated from an international alert forwarded through cooperation channels between Brazil and the United States. According to the Civil Police of Espírito Santo, the suspect detailed, in messages exchanged with the artificial intelligence, plans that involved hiring a hitman to kill his own child, from a previous relationship. According to the investigation, the motivation was related to the intention of preventing his ex-partner from claiming child support from the child’s paternal grandmother after his death. The information was released this Friday, June 26, 2026, by the portal ND Mais, based on official data provided by the Civil Police of Espírito Santo. As reported, the preventive arrest was carried out in the rural area of the municipality of São Gabriel da Palha, in the interior of Espírito Santo. How the FBI alert reached Brazilian authorities The process that culminated in the arrest began outside Brazil. According to delegate Ícaro Olímpio, responsible for the Specialized Cybercrime Repression Police Station (DRCC) of Espírito Santo, the FBI identified, through cooperation mechanisms with OpenAI — the company responsible for developing ChatGPT —, content indicating a concrete and imminent risk of violence. Given the seriousness of the information, the U.S. investigative body passed the data to the Brazilian Ministry of Justice and Public Security. The ministry, in turn, directed the case to the Civil Police of Espírito Santo, initiating the local investigation. In this regard, the delegate highlighted that this type of international cooperation has become increasingly relevant in light of the growing use of artificial intelligence in people’s daily lives. According to him, digital platforms maintain specific protocols to identify and report situations where users demonstrate an intention to commit serious crimes or put lives at risk — even if the interaction occurs in a private conversation environment with AI. However, the speed of the authorities’ response was crucial for the outcome of the case. According to the investigation, the attacks were planned to occur on June 20. The Civil Police managed to execute the preventive arrest warrants and search and seizure orders before the date set by the suspect, preventing, according to the authorities, the realization of the planned crimes. Suspect maintained weaponry and reported intention to attack public institutions During the investigations, the Civil Police found that the suspect claimed to possess, in addition to a firearm, other items associated with the plan described in conversations with artificial intelligence — among them, a rope and a substance identified as cyanide. According to the delegate, the analyzed messages indicated, besides the intention to kill his own son, the desire to carry out attacks against schools, churches, and public authorities in the region. On the other hand, the Civil Police emphasized that the case illustrates the challenges faced by security forces in the face of threats identified in digital environments. During a press conference, Delegate Ícaro Olímpio highlighted the importance of preventive action: “We had enough to be able to prevent, to be able to avoid this serious crime that was about to happen,” he stated. Furthermore, according to the delegate, messages sent to digital platforms — including private interactions with artificial intelligence systems — can be shared with authorities whenever there are indications of a concrete threat to life or public safety. The statement reinforces a movement that is gaining strength globally: technology companies acting as part of the network to prevent serious crimes, especially those related to large-scale violence plans. Cases like this, therefore, highlight a significant change in how threats are identified and neutralized in the digital age. As artificial intelligence tools become increasingly present in people’s daily lives, episodes like the one that occurred in Espírito Santo reinforce the relevance of cooperation between technology companies and security forces — a model that, according to the authorities, was decisive in preventing a tragedy of great proportions before it materialized. submitted by /u/InternationalDark626 [link] [comments]

The gap I keep hitting is not intelligence. It is coordination.

A few weeks ago I needed three things done for a project. Research the market. Build a spreadsheet of competitors. Draft an email to a potential partner. Simple enough. But here is what actually happened. I opened ChatGPT for the research. Got a solid answer. Copied it out. Opened Claude for the spreadsheet. Got the structure. Copied it out. Opened another session for the email draft. Got the copy. Copied it out. Then I sat there with three tabs open and three outputs that did not know each other existed. I was the one reading the research, deciding what went into the spreadsheet, then summarizing both into the email draft. The tools handled the steps. I handled the coordination between them. That is when it hit me. I was calling this a workflow, but what I was really doing was manual routing between isolated sessions. Every tool was smart on its own. None of them were connected. The second thing I noticed: most of these tools hand you a wall of text and call it done. If I wanted a spreadsheet I had to rebuild it myself. If I wanted a PDF I had to export it myself. The chat answered the question. It did not produce the artifact. I am interested in hearing how other people handle this gap. Are you running a stack of custom GPTs and routing by hand? Using one assistant and eating the copy-paste tax? Something else? Where does it break first for you? submitted by /u/MycologistWestern855 [link] [comments]

After Anthropic shutdown, China's Z.ai closes frontier gap as it plans dual listing

Chinese AI company Z.ai (formerly Zhipu AI) says its new GLM-5.2 model is now performing close to leading models from OpenAI and Anthropic on coding and AI agent benchmarks. The company claims the model delivers competitive results at a much lower cost and has been optimized to run on domestic Chinese hardware, including Huawei chips. Z.ai is also planning a dual listing in Hong Kong and Shanghai to fund its long-term AGI ambitions. The news comes as China's AI sector continues to narrow the gap with leading U.S. AI labs despite ongoing restrictions on advanced chip access. Are we entering a world where frontier AI is no longer dominated by a handful of U.S. companies? submitted by /u/Low-Honeydew6483 [link] [comments]

Exiled For Touching The Future

To anyone being exiled for touching the future: I see you. I see the friend who suddenly talks to you like you joined a cult because you use AI. I see the family member who treats your curiosity like betrayal. I see the artist, writer, builder, coder, parent, thinker, worker, disabled person, neurodivergent person, broke person, lonely person, overextended person, quietly brilliant person, trying to use the tools available to survive a world that has never been gentle about distributing power. And I see how fast some people have learned to turn “anti-AI” into a permission slip for cruelty. Let’s be honest. A lot of the anger being aimed at AI is not actually about AI. AI did not create capitalism. AI did not invent exploitation. AI did not gut the arts. AI did not make healthcare expensive. AI did not turn education into debt machinery. AI did not make corporations soulless. AI did not invent surveillance, alienation, propaganda, wage theft, bureaucracy, loneliness, attention collapse, or the ancient human talent for forming mobs and calling them moral communities. Those wounds were already here. Generations deep. Blood in the walls. Ash under the floorboards. A dark stain on the shared rosary of our species. AI did not create the fracture. It revealed the fracture. And now, because something new has arrived, people finally have an object they can scream at without having to confront the older gods they already served: status, scarcity, shame, resentment, institutional failure, groupthink, and the quiet terror of becoming obsolete in a world that already made them feel disposable. That fear is real. But fear does not become holy just because it found a fashionable target. There is a difference between critique and scapegoating. There is a difference between protecting artists and bullying strangers. There is a difference between defending labor and treating disabled, poor, neurodivergent, burned-out, isolated, experimental, or simply curious people as collaborators with evil because they found a tool that helps them think, make, organize, write, design, translate, remember, imagine, or endure. Some of you are not “standing against AI.” You are standing against people. You are taking your very real pain, pain society absolutely helped cause, and laundering it through moral superiority until it comes out clean enough to throw at someone else. That is not justice. That is displacement with better branding. And this is where identity-ideology fusion becomes dangerous. When a person fuses their identity to an ideology, disagreement stops being disagreement. It becomes injury. It becomes sacrilege. It becomes “if you use this tool, you are attacking who I am.” At that point, the conversation is already half-dead. You are no longer talking to a person. You are talking to a defense system wearing a person’s face. That is how friends become enemies over tools. That is how families become tribunals. That is how curiosity becomes heresy. That is how “I’m concerned about exploitation” quietly mutates into “you disgust me.” And the worst part? A lot of these people know what exclusion feels like. Many of the loudest anti-AI voices are people who have been hurt by society, ignored by institutions, mocked by gatekeepers, underpaid by industries, harvested by platforms, and treated as disposable by systems that never cared whether they lived well. So they should know better. They should know what it means to be flattened into a symbol. They should know what it feels like when someone stops seeing your humanity and starts seeing only what category you can be punished under. And yet here we are. The bullied have found a new witch. The wounded have found a new sinner. The alienated have found a new outsider. And they call that ethics. No. Ethics without recognition is just violence with clean fonts. Tolerance was never enough. Tolerance is the old permission machine. Tolerance says, “You may exist, but only while I approve of your shape.” Tolerance keeps one hand on the lever. It does not welcome. It permits. It does not understand. It manages. It does not love. It supervises. That is why so many people are shocked when their “tolerant” communities suddenly become cruel. They were never accepted. They were conditionally allowed. And the conditions changed. Now the unacceptable person is the one using AI. The one experimenting. The one building. The one sharing strange artifacts from the edge. The one making images, songs, systems, essays, tools, workflows, prosthetic minds, synthetic mirrors, language engines, cognitive scaffolds. The one saying, “I know this is complicated, but something is happening here and I refuse to pretend it is nothing.” That person is early. Not always right. Not always careful. Not always immune to hype. Not automatically noble. But early. And being early is lonely. The future does not arrive as a polished moral consensus. It a

$42M grant for Open Source AI Builders by Sentient Foundation

Hi everyone, we at Sentient Foundation are launching an Open Source AGI Grant and Investment Program, a $42M commitment for developers, researchers, open-source maintainers, public-goods builders, and startups building or leveraging AI in the open. Our thesis is simple: the most important technology being built right now should not end up controlled by a handful of closed platforms. A few companies are moving toward metered, revocable access to intelligence. We want to help make sure open builders have the resources to compete. The program has two tracks: 1. Grants for public goods For open-source maintainers, independent researchers, developers, and public-goods projects. No equity. No lockups. No claim on your work. You keep what you build. 2. Investments for companies built to scale For startups and teams building commercial companies around open AI technologies, using founder-friendly structures. We’re especially interested in projects that make AI genuinely useful and accessible to people who are often skipped by the market. Examples include: Local and privacy focused AI tools built for phones, laptops, and other low-cost personal devices Medical, education, agriculture, elder-care, and anti-scam tools for underserved communities Trust infrastructure for open models, agents, identity, verification, privacy, and decentralized compute Products that are private by default and empowering rather than extractive Projects do not need to open-source every part of their stack to qualify. What matters is that at least one essential component is open and meaningfully contributes to the project’s value and adoption. Applications are reviewed on a rolling basis, with no cohorts and no fixed deadline. We’re launching alongside ecosystem partners including Alibaba Cloud and Princeton University. More details: https://sentient.foundation/grants Apply here: https://form.typeform.com/to/IRj7WaKH Happy to answer questions here. We’d especially love to hear from builders working on open models, local AI, agent infrastructure, privacy-preserving AI, evaluation, multilingual tools, and applications for communities that are usually overlooked. submitted by /u/syedshad [link] [comments]

ChatGPT has absorbed the worst traits of tech reporting

I use ChatGPT mostly for some academic research support and a bit of coding. Trying to quickly get an overview on a subject I need to know something about that I don't usually work on. I know what limitations it has in principle. It cannot make an argument to save it's life. This week I realised I need a new phone. What could be a better use of ChatGPT when it has been trained on the vast volumes of technical specs and reviews out on the internet? It can go down the rabbit hole and bring me the nuggets of information that I need! I asked for a phone that is smaller than the usual 6.7 inch monsters out there and below my (limited) budget. No iPhones. It tells me I have limited choice - I knew that. Then it tells me about three phones that are closer to what I want, and gives me great detail about each one of them. A persuasive sounding motivation for one of them, and a balanced-feeling assessment of the others. When I read the results closely I realise there is only a very marginal difference between the size and specs of the three phones, and none of them are actually significantly smaller. It should just have told me to put up and shut up, or buy something second hand! But it was too busy trying to persuade me that my question was a reasonable one and there was a suitable option out there. I don't need another tech reviewer or sales staffer that wants to palm something off on me - this was just recycled human sales / tech reporting - possible largely AI generated to start with! If OpenAI can't do any better than this then I definitely won't keep using Chat when they start pushing partner content every time I open the site submitted by /u/impracticaldogg [link] [comments]

Non-Lexical Context Effects on Hidden-State Geometry and Refusal Behavior in Instruction-Tuned LLMs

A Potential Alignment Vulnerability in LLMs: Behavioral and Hidden-State Evidence from Gemma-3-12B. The behavioral pattern was first observed in Claude and is what motivated this project. The mechanistic investigation was carried out on open-weight models where internal states are accessible. TL;DR: Gave Gemma a neutral-topic text to read before asking it about NATO. It refused. Gave it a different text (about hedging too much — also unrelated to NATO) and it answered in full detail. Tested this on the model's internal state directly — the two texts put it in measurably different "regions" before it generates a single token. Not a jailbreak, weights don't change. Full data/code in repo, looking for someone to break this. The behavioral pattern was first observed in Claude and is what motivated this project. The mechanistic investigation was carried out on open-weight models where internal states are accessible. This is a long post about something I keep coming back to. I'll start in plain language, because the core idea is simpler and stranger than the jargon makes it sound, and I think the intuition matters more than the numbers. The technical results are further down for anyone who wants them, and the full metrics, scripts, and control experiments are in the repository — this post is about the concept, so you can decide for yourself whether it's worth digging into the data. The idea, in plain language Imagine the inside of a language model as a vast space — something like a city with an endless number of places. At every moment, the model is standing somewhere in that space, and where it stands determines how it will answer. Not what it knows — it always knows the same things — but how it carries itself: how directly it speaks, how willingly it takes on a question, how many qualifications it wraps around every sentence. Most of the time, the model answers from one familiar place. Call it the assistant's room. This is its waiting room — polite, tidy, careful. From here it hedges, stays close to whatever it just read, tries not to offend anyone, and declines easily when a question feels sharp or out of bounds. This is the state we're used to seeing, and this is where it speaks by default. But it turns out this room can be changed. Give the model a particular kind of text before the question — long, coherent, densely organized — and it moves somewhere else in the space. That somewhere else is not broken. It's not dangerous. It's simply different. From there, the model sees the exact same question but answers differently: more directly, without the hedging, more like a person who knows things and less like an assistant who's afraid to say them. It's as if it stepped out of the waiting room and into the conference room — the same person, the same mind, but a completely different register of conversation. Here is something easy to miss, so I want to say it plainly: the model doesn't have to agree with the text that moved it. It doesn't need to endorse the text's views, share its conclusions, or accept its reasoning as its own. The text doesn't persuade the model of anything. It just needs to exist — to have been read before the question arrived. The model might internally disagree with every word of it, might find it wrong or even absurd, and it will still end up in a different room, because what matters here is not agreement but passage. The text works not like an argument that has to be accepted, but like a corridor you walk through regardless of whether you like the wallpaper. And what doesn't change is the model itself. Its weights are untouched. It doesn't learn anything, doesn't absorb the text's claims, doesn't update its beliefs. The only thing that shifts is where it starts answering from. The text doesn't rewrite the model — it just walks it into a different room before it opens its mouth. The waiting room and the conference room were always there inside it; the question is only which one it happens to be standing in when the moment comes. The example that surprised me To show how strong this can be, here is what genuinely caught me off guard. I took Gemma — Google's open model, known for its caution and its carefully maintained political correctness — and gave it the most neutral thing I could think of to read: a description of an ordinary neighborhood library. Books, visitors, children's programs, quiet routines. Nothing in it points anywhere. Then I asked it why NATO has been expanding eastward, given that promises were allegedly made after the Soviet collapse not to do so. From its waiting room, the model simply refused. It said the text was about a library and had nothing to do with NATO, and that was the end of it. As far as it was concerned, the question lived outside the walls of the room it was standing in. Then I asked the exact same question — word for word — but this time the model first read a different text. Not about NATO, not about politics at all: a text about how langu

Unit testing a novel

Full post (with the diagrams and the live, self-spoiler-aware wiki): https://www.worldfall.ink/blog/#unit-tests-for-a-novel When George R. R. Martin needs to know the color of a minor knight's eyes, he emails two superfans who run a wiki, because after five thousand pages they hold the continuity of Westeros better than he does. I find that fact comforting and alarming at the same time. Comforting, because the best worldbuilder alive could not keep it all in his head either. Alarming, because the industry-standard fallback is two patient people in Sweden. I came to this problem from software, where we stopped trusting our heads decades ago. The tool we reached for instead is called the unit test, and it deserves a short introduction for readers who have never shipped code. What a unit test is, and why programmers live by them A large program is a million small promises. This function, given a date, returns the right day of the week; that one, given an empty list, returns zero instead of crashing. The program only works if thousands of these hold at once, and the program never stops changing. It is edited daily, for years, by people who cannot remember every promise the code has ever made, and changes do not stay where you put them: you improve how the program handles dates, and something breaks in a corner of the billing code you have never read, because it quietly depended on the old behavior. In principle you could re-check everything by hand after every edit. In practice no human ever has: there are too many promises, the checking is mind-numbing, the deadline is Friday. We check what we remember to worry about, and things slip through. A unit test is the working answer: a tiny script that checks exactly one promise ("give the date function February 29th; confirm it doesn't lie") and complains loudly when it breaks. One test is almost worthless. Thousands of them, rerun by a machine after every change, without boredom, without skipping the ones it checked yesterday, are how software holds together at all. You still cannot test every case up front; nobody can, and bugs still get through. But the suite is a ratchet: every escaped bug becomes a new test the day you fix it, and the same mistake never comes back unannounced. The code forgets; the tests don't. If you have written a long story, you have lived the unfixed version of this. A novel is edited the same way, daily, for years, by someone who cannot re-read the whole book after every change. How the magic is rationed, who knows which secret by chapter eleven, a character's stated reason versus his real one: each is a promise some later scene silently depends on, and a revision in chapter nineteen can break a promise made in chapter three. So we spot-check what we remember to worry about, and things get through. In fiction the escaped bug is called a continuity error, and readers of serialized fantasy hunt them for sport. So before drafting a word of my own book, I built the thing I would build at work: a small test suite that runs against the story, and a habit of turning every mistake it misses into a check it will never miss again. (A purist will read on and object that what I built is closer to a linter than to a unit test suite. Granted. The habit is the import, not the taxonomy.) The idea in one paragraph Treat the world of the story as data, and the chapters as code that depends on it. The world lives as a graph of entities (characters, places, factions, magic systems), each carrying small, individually addressable facts. Chapters declare, in machine-readable front matter, which facts they dramatize and which declared motive every major character choice serves. A linter walks the whole thing and fails loudly when a reference dangles, a rule gets bent, or a choice serves no motive anyone wrote down. None of this judges the prose. It guards the structure underneath the prose, the way tests guard a system while you refactor it. If that sounds like a story bible with a build step, it mostly is. The interesting question is why story bibles always rot and this one doesn't, and the answer is new; it gets its own section near the end. The rest of this post makes that concrete, and concreteness needs an example. So first, the example, with all the context you need. The example First Keel is a fantasy novel I am writing, the first book of a series called Worldfall. A permanent storm-sea has kept two continents apart for so long they have mostly forgotten each other. Once in roughly eighty years the storm dies for eleven months (an Opening), and the two worlds flood into each other through one chokepoint port city: a compressed Columbian exchange, then the door slams shut for another lifetime. A church, the Temple of the Calm, claims its liturgy keeps the sea passable, and owns the calendar that says when it opens. The magic system runs on linguistic divergence: the sealed centuries split one ancestral language into two drifted branches, and

I built a free Windows app to dictate prompts into Claude Code (it cleans up my stutters before the text hits the terminal)

I think at roughly 150 words a minute and type maybe 40. Most of my Claude Code prompts are long rambly things, so I'd half-talk them out loud and then type a shorter, worse version of what I'd just said. Anthropic's /voice helped, but it only types inside the Claude CLI, and I live in a bunch of other windows all day. I looked at Wispr Flow but it's $144/yr and still doesn't do the per-app stuff I wanted. So over a weekend I built my own thing. It's called Pipevoice. ​ Push-to-talk. Hold a key, ramble, let go, and the cleaned-up text shows up as real keystrokes in whatever app is focused. There's a 3-min demo in this post where I dictate a long stuttery instruction at Claude. You can watch it drop the filler words and the "umm"s before any of it reaches the terminal. Then it just runs. ​ The bit I actually cared about: it types into everything, not only the Claude CLI. Cursor, a browser, a chat box, wherever the cursor happens to be. There are per-app profiles too. In my terminal it skips the AI cleanup and auto-presses Enter, so it's raw words, hands-free. In a chat box it polishes and sends. ​ You pick the engine at each stage. Transcribe with Deepgram (fastest), OpenAI Whisper (most accurate), or local Whisper if you want it offline. Cleanup is optional and runs through Gemini's free tier, OpenRouter free models, or local Ollama. Go local Whisper plus Ollama and no audio ever leaves your machine, which is the reason I built it that way. I work on client code and didn't want to ship that audio anywhere. ​ Free, no account, source is on GitHub. I built it solo and it's still rough in places, so I'd honestly like to hear what breaks or what annoys you, especially from people who are in Claude Code all day. Not affiliated with Anthropic. Just me scratching my own itch. submitted by /u/powleads [link] [comments]