making machines dance | (F/ai - F26)
Paris, France
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long story short, you can’t kill me because I already died once
"It's actually almost impossible to sound smart when you're kind."
@MaxSebti on the near-death experience that changed how he builds, why a broadcast team at IBC picked up the phone mid-meeting, and what he'd say to anyone about to walk away from Bittensor.
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Arno San enters the room
next batch of stations to be deployed with @manakoai is going to allow us to leverage a lot more @webuildscore models.
- 4 motorway stations. beasts with 40+ cameras each.
- that’s 10x more cameras than on unmanned stations.
- massive fuel forecourts, EV charging bays, car wash, restaurants, supermarkets, coffee areas.
the second best news…
is it’s with a new signed client 👀
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this
Innerworks (company behind Redteam) is a VC-backed company incorporated in the UK. Lying about enterprise customers would literally be defrauding their own investors. Their VCs have seen the actual agreements.
The other point is that disclosing customers is not free. Companies charge steep discounts just to use their logo on a website. Anyone who's operated a business knows this. Plus, they're in the security space, where announcing who's protecting you is itself a security risk.
But anyways, we meet with the team weekly and are still heavily aligned, invested, and comfortable on this drawback. Be patient.
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will proudly represent the bald guys
Two machine learning teams. One #Bittensor subnet slot. You decide who gets it.
At pitchtensor, hosted by @bitstarterAI, two aspiring teams pitch to @macrocrux, @KeithSingery, @maxsebti, @EvanMalanga + @willobrien - then you choose the winner in a live crowdfund, online + in the room.
Live from the Exploit stage: luma.com/exploitsummit26
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there are 3 key steps to overcome for any early stage startup.
They define the ability to move from “a cool concept” to “a promising business”.
1. the ability to sell
- product ready or not, the ability to have a client send money to your bank account for what you say you can do is the #1 priority for early stage startups.
2. deliver on the promise
- building a solution that can actually show to your client you’re not full of shit and can actually deliver what you claim.
3. production readiness
- building a prototype for a demo and having a solution ready for large scale deployments are two different worlds.
last friday @manakoai was a cool concept, it is now a (very) promising business.
check, check, and check ✅
who dares wins.
This weekend the team brought 15 stations online across Lyon, France.
Three days of work. Around five minutes end to end for each site.
Deploying computer vision to a physical location usually means weeks of integration. New cameras, a site visit, cabling, sign-off.
Every one of these 15 stations went live on the cameras it already had. No new hardware, no rewiring, no site visits. We point Manako at the existing feed and it starts watching and alerting.
The reason we did 15 at once was to see how deployment holds up across different setups. No two stations are the same, camera angles, how the feeds are wired, the lighting, the layout.
Doing a batch like this confirms the system handles real-world variance, rather than just guessing from one clean pilot. We have our baseline, and we can now take this speed and scale the rollout to any site.
Real World. Real Action.
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Last weekend we hit the road with @arnod3f carrying one question in the luggage:
Can we make @manakoai truly scalable?
3 days. 700 km. 2 fuel stops.
The answer is yes.
Our team has built one of the most scalable, seamless, plug-and-play Vision AI solutions out there.
You receive the box.
Plug in power and network.
That’s it.
Your site is ready, and immediately enhanced by our vision agents (powered by @webuildscore vision models).
No complex rollout. No weeks of integration.
Just unbox, connect, and go.
This weekend the team brought 15 stations online across Lyon, France.
Three days of work. Around five minutes end to end for each site.
Deploying computer vision to a physical location usually means weeks of integration. New cameras, a site visit, cabling, sign-off.
Every one of these 15 stations went live on the cameras it already had. No new hardware, no rewiring, no site visits. We point Manako at the existing feed and it starts watching and alerting.
The reason we did 15 at once was to see how deployment holds up across different setups. No two stations are the same, camera angles, how the feeds are wired, the lighting, the layout.
Doing a batch like this confirms the system handles real-world variance, rather than just guessing from one clean pilot. We have our baseline, and we can now take this speed and scale the rollout to any site.
Real World. Real Action.
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🙏
Proud of you @MaxSebti your pitch was great. One of the firsts talking about Decentralized Ai in front of this kind of audience. This is necessary to be game changers. @kusanagi_vntrs @opentensor bittensor:native
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preached decentralised AI in front of the largest French PE and VC funds (hundreds of billions under management in the room).
main takeaways from the COO of a multi-billion-dollar manufacturing company:
- efficiency is the ultimate goal.
- all big-data LLM pilots have been cancelled across all verticals: no clear use cases, no productivity spikes.
- vision is the only vertical where he and his industry counterparts know value can be created, so they want to double down on it.
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