... through an API. One prepaid balance works for both. After 10, 50 and 100 successful videos, the per-video price falls to $2.20, $2.00 and $1.50. Confirmed technical failures restore the charged balance. I put the full, uncut outputs on public watch pages so you can judge the result before paying: https://seedancecheap.com/exampl... and https://seedancecheap.com/exampl... . The site is live at https://seedancecheap.com and the Product Hunt launch is scheduled for October 1. The current checkout is crypto ... ... funded, starting at $5. What would stop you from trying one video: that payment method, output

... working on making Ski more comfortable to use for people who don t speak English all the time. The latest update adds more control over both voice input and voice output , with platform-specific improvements: macOS: More languages available for voice input in High and Ultra modes Windows, Linux: Ultra (default) mode can automatically detect the language being spoken Voice output

... read restrictively, because it takes transparency away. Do not lean on "everyone knows it's AI." That's the argument that gets read narrowly. Article 50(2): if your system generates synthetic audio, image, video or text, the outputs must be marked in a machine-readable format and detectable as AI-generated. Some things are out of scope - short sequences of letters or numbers, source code, outputs

... latest model in the Claude 5 family and part of the Mythos-class tier tuned for long agentic work, the kind where a model has to plan, reach for the right tool, run code, check its own output, and stay coherent across a task far too long to hold in your head. The headline number is the one every launch post will lead with: on Terminal-Bench-Science , a benchmark for autonomous scientific computing, Fable 5.1 scores 52.6% more than ... ... double Fable 5 s 24.7%, and comfortably ahead of Opus 5 s 29.0%. It ships with a 1-million-token context window , up to 128K tokens of output

... image models plus Veo 3.1 and Seedance 2.0 behind one studio. Until recently every generation record stored the prompt and the output file and nothing about which model produced it. When there was one model that was fine. With a router it means every quality complaint ends in a shrug. Someone says the face drifted between the image and the video, and I can't tell them whether that was the model, the seed or the upscale step ... ... reproduce it either because the router might pick differently next time. So the report goes in a doc and dies there. Writing model, version and the reason the router picked it onto every output

... other builders here handle this, because I don't think there's a settled answer yet. If your product generates text, summaries, descriptions, code comments, whatever using an AI model somewhere in the pipeline how much of that output do you actually check before a user sees it? Full verification pass? Spot-checks? Trust the model and fix things when users report them? Something automated? What's making me ask: the failure mode that worries me most isn't obviously ... ... broken output

... button before the click instead of in a docs table. Boring fix, and it cut the where did my credits go messages more than any price change we made. The part I haven't solved is failures. A bad output still burned the compute. Refund it and people retry until they like the result. Don't refund and credits feel like a slot machine. We refund and eat it, which won't scale. Curious where others have landed on charging ... ... output

... local Facebook group, people from my country had a pretty interesting discussion about how they justify charging a higher price for an output generated by AI. Since AI came on the scene, we kind of expect the job to be done faster with less effort and the result is satisfactory. Does that mean the buyer has the right to ask for a lower amount? I don t know. Because several parameters come into the process: using state ... ... models an expert human who enters the prompts their time babysitting the outputs

... whenever I see and suspect that something is generated by LLMs, I feel that the person on the other side has slopped it a bit (and subconsciously I perceive it as "something less"). I have to say that some outputs that look really high quality and I have trouble distinguishing them from "handwork" are perceived as completely normal (at least for me). As equal human work. How do you view things generated by artificial intelligence? Maybe it's true that ... ... behind the output

... High-risk got pushed to December 2027 and the whole timeline read as "delayed." Article 50 didn't move. It applied on 2 August, it catches you if an EU user sees your output, and the ceiling is 15m or 3% of worldwide turnover. Two and a half weeks ago a rule started applying to most people reading this, and the coverage around it said the opposite. Here's what happened. The Digital Omnibus was published in the Official Journal ... ... with being high-risk. It attaches to what your product does, not what category it falls in. And the scope is extraterritorial in the way people always underestimate a provider outside the EU is in scope if the output

... built to protect them is just annoying them. Background: the thing I work on generates a file for you in the browser. For a long time I stored nothing at all the input was discarded the second the output existed, and the output

... them. So now the version I'm actually proud of sits behind a field most people scroll past, and the version most people meet is the weaker one. Four options, none of which I love: Make it required. Best output, worst first run. Ask for it on the second run, once they've seen a result and have a reason to care. Show a before/after of both outputs

... When we set up credits, one credit meant one generation. Easy to explain, and wrong. A generation isn't one thing. The same request routed to a cheap model versus an expensive one can differ by an order of magnitude in what it actually costs us, and a retry after a bad output

... Analyzed a day of token logs across an autonomous coding agent setup running on internal codebases. The raw count: 769M input tokens vs 7.4M output

... answers, it responds, it does the thing. And you sit there with a genuinely hard question you have no tool for: is it any good? That question used to answer itself. Old software was deterministic same input, same output, and you wrote a test that either passed or failed. AI features don't work like that. Ask the same thing twice and you get two different answers, both plausible, one subtly wrong. "It ran without erroring" tells you almost nothing ... ... real inputs actual things users typed, not inputs you imagined. For each one, write down what a good response looks like, in your own words. When you change a prompt or a model, run all twenty and read the outputs

... Nobody generates once. In a creative tool the real unit is six attempts and one keeper, so credit pricing quietly charges people six times for the thing they actually wanted. The arithmetic can match per output pricing exactly and it still feels worse, because every failed try has a visible price tag on it. The version that hurts most is the near miss. An output

... metrics one source tracked, but Claude Code holds an 88.6% SWE-bench Verified score by another measure. The benchmarks genuinely don't agree with each other depending on methodology, which is its own lesson. Price-per-output tells a different story than raw capability. Cursor's Composer 2.5 lands in the top three on capability index while running roughly 10-60x cheaper than the leaders, so if you're optimizing purely for $/task rather than fewest tokens per task ...

... that the information is missing, it's that it's one interaction away at the exact moment someone is going fast. I spent an evening this week chasing a bug my own tooling had reported correctly, in output I had skimmed past. Anything behind a click gets read when things are calm and skipped when they aren't and it's the second case where the flag matters. Comment from Vitalii Timonin(@vitalii_timonin): The part about the agent being ... ... hustle SEO tools, so worst case is a bad diff I catch on review. No customer sends, no charges, nothing touching production without me in the loop first. So "reach" for me caps out at one person: me, reading output

... been thinking about a mistake we make when discussing AI-agent context: We treat all context as if it serves the same purpose. The usual advice is straightforward: Remove unnecessary context Summarize long outputs Reduce token usage Make the agent cheaper That works until the unnecessary text contains the exact line needed to apply a patch or until a nicely summarized tool output

... doesn't even have to be a big dramatic failures, more the small moments where something clicked and you went from trusting the output by default to double checking everything. For me it was watching an agent confidently rename a function across twelve files, then leave the original function untouched in a thirteenth file it apparently didn't search, with zero indication anything had been missed. It wasn't even a hard case, the file just wasn ... ... task is complete, but quietly skips part of the work and doesn t flag the uncertainty. The mistake itself is usually fixable the bigger trust issue is the false confidence. It definitely changed my workflow. I now treat agent output

... smaller. Same answers come back. Where My Tokens Were Going I ran copium stats --period month after the first week and saw the breakdown: Category Tokens/Day (Before) Tokens/Day (After) Savings File reads (duplicates) 180K 12K 93% Tool outputs (JSON) 320K 64K 80% Build logs 95K 14K 85% Search results 150K 30K 80% Conversation history 200K 140K 30% Tool schemas 45K 8K 82% Total 990K 268K 73% Almost a million tokens per day, down to 268K. The quality gate ensures nothing ... ... important was lost. The Cost Breakdown Anthropic Claude Sonnet pricing: Input: $3 per million tokens Output

... Before we launched Echonos, the single most common question in our early-access feedback was not about quality or price. It was: "Can I use this commercially?" We had assumed creators would ask about output resolution or audio format compatibility. The IP question blindsided us, and it took several weeks of research, legal review, and careful policy drafting to answer it clearly. This is a maker post about what we actually found and our full breakdown of AI music video ... ... stronger than most creators realise. When a creator writes a prompt choosing the visual mood, the colour palette, the motion energy they are making creative decisions. The more specific and directed the prompt, the stronger the argument that the output

... Apple Music on Mac can show Lossless but what is your Mac actually outputting? Audex Trace shows: the actual playback format bit depth and sample rate your Mac output

... month that I've been here, I've been noticing a pattern in a lot of launches - strong demos, polished UI, clear outputs of "what it does." But when I ask myself "What problem does this solve?" I sometimes have to dig for the answer. (I come by that thinking honestly - I've spent 33 years building and fixing businesses, so this is the lens I can't turn off.) The products where the problem is obvious are the ones ... ... more than any UI polish did. So yeah, I d say naming the problem is the actual unlock. The demo just has to not get in the way of it. Comment from Viraj Parab(@viraj_parab): StartupHQ to organize competitor

... solo founder, these are the five tools I use almost every day: ChatGPT VS Code GitHub Ledger (I dogfood my own invoicing app.) Google Search Console They don't make building easy, but they definitely make it possible. Comment from faheem asad(@faheem_asad): My daily toolkit is pretty simple, but I rely on it constantly: ChatGPT brainstorming, writing, and problem-solving. VS