Fractional CMO | AI Education & Art | Helping build & scale MVPs fast | GTM playbooks that hit PMF + raise 4-$16M+| AI & Performance Growth Precision
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Joined June 2008
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Unlocked a crazy use case with Nano Banana Pro.
Take your complex papers, in this case I fed all the research I had done for Meta Ads and Andromeda update in 2025.
And use this prompt: Take this paper and transform it into the image of a professor whiteboard image. Diagrams, arrows, boxes, and captions explaining the core idea visually, use colors as well.
P.S - You can unlock unlimited Nanobanana Pro with @higgsfield_ai this week.
Ayush 🦞 retweeted
We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work.
At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated.
We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.
Ayush 🦞 retweeted
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
Ayush 🦞 retweeted
We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:
Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:
Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:
Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:
Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!
In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.
Ayush 🦞 retweeted
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
Ayush 🦞 retweeted
You’ve been asking, we’ve been building!
Google Flow is now available on iOS.
It’s now easier than ever to pull inspiration from your camera roll or photos and videos captured in the moment. And with cross-device project syncing, you take your creative work wherever you go.
🤖 Made with AI
GPT-6 Astra demo just dropped. I really like what I’m seeing overall, but the slides in the demo were… kind of meh.
Taste is subjective, of course. But I’m stoked that as a marketer, I can look at a demo from one of the most advanced AI systems out there and think: I’d have made those slides better.
That’s a pretty cool feeling.
Ayush 🦞 retweeted
The most useful thing I've learned about food mastery is that eating bad things is never, ever worth it. You never feel good after. Only regret. Once you fully internalize this, you can simulate the experience and then muster the power to overcome the primal desire.
ElevenLabs just dropped their MCP inside Claude. You can manage voice agents and review call performance without leaving your session.
I wrote about this two days ago when OpenAI put their agent inside Claude Code. Same pattern. Every lab is building inside every other lab's product now.
I've been running MCP workflows for months. They eat through Claude Pro credits fast, the token cost is real. But the direction is obvious. Your AI stack is collapsing into one window.
Ayush 🦞 retweeted
FABLE 5 CAME BACK NERFED.
We re-ran the July 1st version of Claude Fable 5 on BridgeBench.
The results are brutal:
Debugging: 86.2 → 25.9
Refactoring: 73.6 → 38.4
Hallucination: 75.9 → 61.7
The new guardrails are kicking in on way too many tasks and falling back to Opus 4.8.
This is not the model that got banned.
Anthropic owes everyone an explanation.
Ayush 🦞 retweeted
We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5.
We'll begin restoring access tomorrow, and will share an update soon.
We’re grateful to our users for their patience, and to everyone who worked with us on redeploying the models.
OpenAI put their agent inside Anthropic's Claude Code.
A year ago every lab wanted you locked into their stack. Now they're building inside each other's products.
You show the AI a workflow once. It learns it. No more writing SOPs.
Ayush 🦞 retweeted
Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API.
Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls.
Try it: sakana.ai/fugu 🐡
I was wrong about prompts.
I spent months optimizing the wording, trying to find the perfect model, testing every new AI tool that dropped.
Turns out I was solving the wrong problem entirely.
My biggest unlock wasn't better prompts. It was better context.
Before I touch any AI tool now, I open WisprFlow and dump 1-3 pages of context: what the project actually is, who it's for, what good looks like, where the landmines are.
Then I fire off research agents before I even sit down.
By the time I'm in the chair, the market data and reference material are already waiting.
The result: full deliverables in one focused session. Things that used to take a day now come out of a single sitting.
Context first. Prompt last.
Ayush 🦞 retweeted
You are still writing AI prompts.
The best builders stopped months ago:
They design loops that do the work, check it, and improve while they sleep.
Boris Cherny, who built Claude Code: "I don't prompt Claude anymore. My job is to write loops."
Here is the 6-part loop I run (no code):
1. Write your standards once
- Run /init to create a CLAUDE.md file.
- Add your voice, rules and banned words.
- It loads on every single job.
Example: "/init Put my voice in CLAUDE.md: British English, no em dashes, my hooks."
2. Goal and a finish line
- State the outcome you actually want.
- Give it a rubric to score against.
- "Don't stop until it scores 95/100."
Example: "/goal Write a post, score it on my rubric, don't stop until it scores 95/100."
3. A team does the work
- Strategist picks the angle.
- Writer drafts the output.
- Designer lays it out.
Example: "/agents Make a team: strategist, writer, designer. One job each."
4. Add a checker (the eval)
- A separate agent scores each draft.
- Under 95, it fixes one thing and reruns.
- It never marks its own homework.
Example: "/agents Add a QA agent that scores each draft out of 100 and rejects under 95."
5. Let it remember
- Every correction becomes a rule.
- Saved to a lessons file for next time.
- It creates more like me each month.
Example: "/memory Save every correction as a rule, and reload them each run."
6. Put it on a schedule
- A Routine runs the whole loop.
- On the cloud, laptop closed.
- Wake up to finished drafts.
Example: "/schedule Run my content loop weekdays at 7am, leave drafts to review."
You already run half of this.
Close the loop. It improves without you.
I wrote everything in one free guide.
Grab it here
charliehills.substack.com/p/…
Repost ♻️ to help someone in your network.
P.S. Still prompting, or building loops?
