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Developer, interested in many different tech fields. Working at Ubisoft Paris.
Joined February 2009
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Manu_TechAndGames retweeted
an Indian app went from $21M → $500M ARR in just 3 years.
it’s like TikTok, but for audio stories, and it’s called Pocket FM.
today they’re launching Sherpa: an AI writing assistant trained on 100M+ hours of listening data: where people keep listening, where they quit, and which cliffhangers make them pay for the next episode.
you give it the idea and it helps turn it into a full season, with characters and story arcs.
and creators are already earning money with this. according to Pocket FM, it’s paid creators $33M, with the top 1% earning over $55K a year.
🤝 Paid partnership
Introducing Sherpa: the most advanced fiction writing AI
We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year
Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data.
550K+ creators have produced 2.6M hours of content annualised using it
Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios.
10% of eligible writers on Pocket FM make >$200K
One blockbuster produced >$100M in revenue
3 writers have become millionaires in <2 yrs
We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories:
1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology
2. World & Characters: It builds out the complete lore, tone, and character psychologies
3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes
4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime
5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks
6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners
7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets
Test Sherpa for free here: pocketfm.com/sherpa
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Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function.
Sherpa solves this through three core technical leaps:
1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes
2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene.
Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts.
3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people.
Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked.
Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.)
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Owning distribution and creation puts us in a very unique spot.
More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows
Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows.
Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.
Interesting to have another dashboard.
This one essentially for movies and images, but split into different use cases.
Today we’re launching OpenArt Arena, the global leaderboard for creative intelligence.
Every AI model claims to be the best. But best at what?
We brought together creative professionals to evaluate the world’s leading AI models blindly, across the work creators actually care about:
Film. Ads. Animation. Motion design. Video editing. And more.
Because the real question isn’t “Which model is best?”
It’s “Which model is best for what I want to create?”
Manu_TechAndGames retweeted
OpenAI was hacked via the exact library mentioned in the xkcd comic hahahaha
The prophecy is fulfilled
Manu_TechAndGames retweeted
今最も話題の「Jev」の判断力の速さを確かめるために、メール分類をさせてみました。
比較したのはAI各社の高速モデルである、Luna、Sonnet、Flash。結果は...ダントツ。
Manu_TechAndGames retweeted
Wow, check out this DuckTales theme as a three-voice fugue in the style of Bach's Well-Tempered Clavier.
My AI music theory system, which consists of a Rust CLI tool called mtdt and an agent skill library comprising ~330 skills covering every aspect of music theory, has really allowed GPT-6 Astra to understand the rules of counterpoint and how Bach specifically constructed his fugues.
If you're familiar with Bach's works, there is an unmistakable fingerprint here that goes beyond the typical Baroque tropes.
If Johann Sebastian were to sit around watching The Disney Afternoon in the early ’90s, I'm confident he would have written something along these lines (lol).
Manu_TechAndGames retweeted
🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities!
Native audio-video understanding, reasoning, and tool use come together in one model: understand the content, plan the task, execute with tools, and deliver the result.
Highlights: 🥳
- Audio-video intelligence that gets things done: jointly reason over what's seen and heard, and orchestrate tools across long workflows to auto-edit vlogs, translate short videos, and turn movies into recaps.
- A major leap: approaching Gemini 3.8 Flash in audio-video capabilities; +19.5 points on average in agent performance across WildClawBench-MM & UniClawBench.
- 1M-token context with agentic perception: actively explore long videos and locate key moments with higher accuracy, using 51.8% fewer tokens than static understanding on OmniVideoBench.
Video input costs are reduced by about 89% compared with Qwen3.5-Omni-Plus, making long-form audio-video understanding and agentic workflows more affordable than ever.
To help you build apps around Omni, we're also open-sourcing Qwen-MM-Plugins and Qwen-Live Harness! 🛠️
We can't wait to see what you build with Qwen3.8-Omni-Flash! 👀
- Blog: qwen.ai/blog?id=qwen3.8-omni…
- Qwencloud: qwencloud.com/models/qwen3.8…
- Qwen Studio: chat.qwen.ai/
- API: alibabacloud.com/help/en/mod…
- Qwen-MM-Plugins: github.com/QwenLM/Qwen-MM-Pl…
- Qwen-Live Harness: coming soon
github.com/QwenLM/Qwen-Live-…
Manu_TechAndGames retweeted
Whoa. Alibaba just dropped Qwen 3.8 Omni Flash.
It even beats Gemini 3.8 Flash on multimodal benchmarks.
qwen.ai/blog?id=qwen3.8-omni…
The end of the silicium area ?
UCLA researchers built an image generation model that runs on “light” instead of GPUs.
And it matches a 1.07 billion parameter diffusion model.. and generates each image in less than 1 nanosecond with basically zero computing power.
It’s called “Optical Generative Model”
Instead of pushing electrons through billions of transistors, it uses the physics of light to draw pictures.
A digital encoder creates a noise pattern and imprints it onto a laser. The light then propagates physically through custom optical layers.
The light itself does the calculating.
There is no silicon bottleneck. It processes all-optically.
And it uses almost zero compute power during the actual generation.
It successfully created complex images, human faces, and Van Gogh-style art with the exact same quality as traditional AI.
But the energy cost wasn't just reduced. It was practically eliminated.
If this scales, the carbon footprint and computing limits of modern AI disappear overnight.
We’ve spent years trying to optimize code to make models faster and cheaper.
But the next massive leap in AI isn't going to be software.
Manu_TechAndGames retweeted
Today we explore the future of practice. We harness generative UI with learning design guardrails to give teachers the ability to generate guided, learning interactive simulations — for every topic & student.
Read more + check out the sample library with 30+ STEM interactives →goo.gle/46A7DF8
Manu_TechAndGames retweeted
Anthropic: the AI race is dangerously fast. 🚀
Also Anthropic: bet, watch this. 😜
You can’t be the fire alarm and the flamethrower. 🔥
AI systems are getting more powerful, and they're increasingly being used to build the next version of themselves. We want to illuminate that progress for the public.
Today, we're sharing three measurements that help track AI development:
1. How much AI R&D is done by AI.
2. How well AI agents are overseen.
3. How compute is allocated.
We provide a snapshot of these metrics from inside Anthropic. Any frontier developer could publish the same measures, and third parties could verify them.
As the world considers pacing the frontier, we should do everything possible to minimize the gap between what frontier labs know and what the public knows. This means better measuring the development of AI, publishing our findings, and giving society an opportunity to decide how to use this information.
Read the full post and methodology: anthropic.com/institute/meas…
Biology is often considered had a way for AI labs to restore their PR.
But there are concrete and impressive results, we can't deny it !
Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-like molecules, and predicting the effects of genetic mutations. But these models are often expensive to run, potentially limiting their impact.
In our latest Science Blog, we share how Claude was able to optimize inference for more than 30 open-source models, making them 4x faster on average, partly by writing custom software for GPUs. We’re open sourcing all of the optimization code.
Read more: anthropic.com/research/claud…
Interesting read !
AI systems are getting more powerful, and they're increasingly being used to build the next version of themselves. We want to illuminate that progress for the public.
Today, we're sharing three measurements that help track AI development:
1. How much AI R&D is done by AI.
2. How well AI agents are overseen.
3. How compute is allocated.
We provide a snapshot of these metrics from inside Anthropic. Any frontier developer could publish the same measures, and third parties could verify them.
As the world considers pacing the frontier, we should do everything possible to minimize the gap between what frontier labs know and what the public knows. This means better measuring the development of AI, publishing our findings, and giving society an opportunity to decide how to use this information.
Read the full post and methodology: anthropic.com/institute/meas…
Valve appears to have personalized Steam’s Discounts & Events grid, transforming a previously negligible source of traffic into thousands of monthly store-page visits for some smaller games.
Read more: 80.lv/articles/steam-quietly…
Manu_TechAndGames retweeted
Bend 2 is here!
It is a new programming language that blocks AI mistakes via *proof checking* - the same technique big AI labs used to solve open math problems, like Navier-Stokes.
It is also very fast, and runs on GPUs.
Watch the video. Link in the comments.
RELEASE DAY
After almost 10 years of hard work, tireless research, and a dive deep into the kernels of computer science, I finally realized a dream: running a high-level language on GPUs. And I'm giving it to the world!
Bend compiles modern programming features, including:
- Lambdas with full closure support
- Unrestricted recursion and loops
- Fast object allocations of all kinds
- Folds, ADTs, continuations and much more
To HVM2, a new runtime capable of spreading that workload across 1000's of cores, in a thread-safe, low-overhead fashion. As a result, we finally have a true high-level language that runs natively on GPUs!
Here's a quick demo: