Founder @LINCInnovations. Building where agentic software meets physical hardware: SniperPulse, LOCI (Spatial MCP), Nerve. F3 Redmond.
Washington state
Joined February 2025
- Tweets193
- Following210
- Followers33
- Likes1.3K
All three MCP servers from our mobile-agent-bench stack are now public under Apache-2.0: nerve (48 device tools), crucible (8 quality-gate tools), and spectra (14 debugger tools).
lincinnovations.com/blog/cru…
linc-spectra (Apache-2.0): github.com/LincForge/spectra
linc-crucible (Apache-2.0): github.com/LincForge/crucibl…
linc-nerve (Apache-2.0): github.com/LincForge/nerve
600-run benchmark grids: lincforge.com
Andre Burnt retweeted
Do you work in physical AI, in academia or industry?
I’m gathering firsthand perspectives for @CoreMatter_ research: what’s improving, what remains difficult, and what would help you make progress.
Take the survey: tally.so/r/X5x64g
Andre Burnt retweeted
Jean-Denis Greze, founder of @TownAI, argues AI isn't diffusing because most people aren't tinkerers: a text box isn't enough, so the product must both identify the value prop and perform the work for them.
"There's this business that rents surfboards on an island to people and then uses Town to inspect surfboards and little beach buggies, whether they've been damaged, to decide whether afterwards the person should get their deposit back."
"What makes it work there is the tinkerer... The unlock is, is there someone over there that gets AI enough to wanna tinker a solution to their problem?"
"And the problem with that is that there aren't enough tinkerers in the world, or there aren't enough people that have the time to be tinkerers to help their business. And I think that's why AI is not diffusing."
"Instead what you're doing is you're using [AI tools] as a replacement, as like a better Google search."
"If you can make a non-tinkerer into getting the same value as a tinkerer... you could be a ginormous business today because AI can solve so many business problems. You don't need more intelligence. The intelligence is there."
@jgreze @arampell
Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, brought in nearly $1M in revenue its first year, and he projected $2.5 to $3M the next. He shut it down anyway.
The reason: in the age of AI, it was never going to be venture scale. Incumbents could use the same technology to automate tax prep for their existing customers faster than a startup could win market share. Tax is also sold once a year, so he had about a one-month window to pivot before the door closed.
The pivot became Town, an AI assistant that works from your email and calendar. It found product-market fit fast enough that Ben Horowitz messaged a16z's Alex Rampell to say it "changed my life."
In this conversation, Alex and Jean-Denis cover:
- How to know when to kill a business that's working on paper
- Why Town competes with OpenAI and Anthropic, the companies it buys its tokens from
- Why the "forward deployed engineer" model gets worse as AI gets better
- How a $1/month AI routine can save a restaurant thousands
- What agents mean for Amazon, advertising and the future of shopping
0:57 How Ben Horowitz discovered Town
2:20 Why Town started with AI tax prep
3:55 Why tax couldn't be venture scale
6:30 Shutting down a working business
10:24 The customer ask that became Town
13:35 Why Town built for users before IT
16:18 AI's iPhone moment at work
21:28 Jevons paradox for executive assistants
23:13 Town as a "token allocation machine"
28:00 How Town tailors itself to each job
34:03 Competing with your token suppliers
42:24 Staying off the AI labs' radar
44:42 Why AI isn't spreading evenly yet
52:32 How agents break the ad model
55:35 The friction humans actually enjoy
57:05 Why supply aggregators lose to agents
YouTube: youtu.be/o_5f4N6Nq80
@jgreze @TownAI @arampell
At 300 km, a satellite sinks about 140 m every orbit. That drop is a measurement of the air it flew through.
We simulated calibrating a cheap, drifting vacuum gauge against GPS orbit decay for @NASA TechLeap's Orbital Clarity Challenge.
Need a vacuum-lab partner before Oct 28.
Across 14-day synthetic NRLMSIS 2.0 runs (gauge starting 27.4 % high):
Lock-in: one orbit (0.06-0.07 d) at 300-500 km, 0.47 d at 600 km (good POD).
Precision: 1.1-3.4 % at 30 s.
Drag-model tradeoff: 10 % ballistic-coefficient error -> 9.1 % density bias (4.6 % across 5 satellites).
Caveat: simulation in a model atmosphere, not flight data. Truth and gauge share one model. At 600 km with COTS GPS on 1-orbit arcs, lock-in is marginal (median 2.2 d, max 12.1 d).
Software alone cannot enter. The gauge has to fly. If you run a vacuum chamber (or work on satellite drag / neutral gauges), please DM me or try to break our assumptions:
Code + 32 tests (MIT): github.com/LincForge/clarity…
YouTube (4K): youtu.be/0Iho34Nu2k0
cc @TSKelso @TamithaSkov @DJSnM #SmallSat #SpaceWeather @SpaceX
Six demos at @AITinkerers Seattle Games & AI night. The number that stuck: studios spent $25B on player acquisition in 2025 for 0.8% install growth. The answer on stage was the real game, playable in a browser tab. 60-second recap.
On stage: @trevordilley6 (DevSwarm), Victor Hurdugaci (Ballard Games), @gemisis (Leviath), @tvalc (Makko), @danhiester (PostRetro), @reddmachine (Contra decision models). Hosted by @craigrrob. Full version: youtu.be/oa8cpZQTkf0
Then Opus 5.5 turned the night into an arcade: 7 microgames, one per demo and hosted on Charming. Pin files before compaction, light a Quake-era room, survive Contra at 2-bit weights. Plays on a phone: charm.ing/steady-otter-5524/…
Andre Burnt retweeted
1/7 After we published mobile-agent-bench v2 (44/45), the fair critique was obvious: we fixed bugs on tasks we had already seen, and competitors had shipped new versions since v1.
So we ran v3: 200 new runs on a Galaxy S24 + Galaxy Watch7 (Android 16).
SniperPulse Daily Hold #1 4/5
🔴🎯🟢🎯🎯
daily.sniperpulse.com
Andre Burnt retweeted
Day 1 of killing 1000 apps.
Today it's Linktree and link-in-bio sites.
Customize it exactly how you want with Claude, ChatGPT, Muse, or whatever.
Then publish yours to Charming. (It's free.)
Today's body count: 61/1000
Follow me for our next drop.
Charming is live, and I'm grateful it exists.
My daughter and I race on our watches. I asked my AI for a scoreboard, and Charming put it online.
Scores go from the watch straight to the board.
Software for one family. Thanks @mrmagan_
charm.ing/steady-otter-5524/…
If an AI agent can't find your pricing, you never make the shortlist.
Best line from @AITinkerers Seattle GTM night (@Apurva, Optimly). Also on stage: @ashbhoopathy, @weiler, @jgordner, @petermdenton. Thanks Dan Moore (@toobulkeh) for hosting.
3 takeaways in under a minute 👇
Andre Burnt retweeted
The seventh edition of our Top 100 Consumer AI Apps is here, including a new dimension this time: what consumers are actually paying for.
a16z's Elena Burger sits down with Olivia Moore and Josh Elman to unpack the current state of consumer AI:
The data reveals a striking power-user economy. Only a small share of consumers currently pay for AI, but among those who do, spending is heavily concentrated at the top. Olivia and Josh discuss why developers, creators, and other power users dominate spending today, and why subscriptions may not be the business model that ultimately brings consumer AI to everyone.
They also dig into the rise of personal agents, the different trajectories of ChatGPT, Claude, and Gemini, how ads could reshape AI economics, and the enormous amount of consumer white space still left to build, from shopping and entertainment to social, dating, and marketplaces.
00:56 Inside the 7th edition
05:55 Muse and Instinct's launch numbers
09:14 The privacy wall for personal agents
14:39 The AI spending power law
20:20 Why subscription is the wrong model
23:51 OpenAI's $1B ad run rate
29:21 ChatGPT vs Claude vs Gemini
32:30 Where creative tools still win
45:12 The white space: dating, shopping, entertainment
YouTube: youtu.be/aCvrzhwUxg0
@omooretweets @JoshElman @VirtualElena
The seventh edition of our Top 100 Consumer AI Apps is here.
New this time: a revenue leaderboard, alongside the usual web and mobile traffic rankings.
Three years ago we published the first edition. ChatGPT was #1, Claude was unranked, and the entire category was chatbots, image generators, and not much else.
In today's edition:
- ChatGPT still holds the throne, now with 1B+ monthly actives on mobile
- Claude has climbed to #3 on web with nearly 1B monthly visits
- The category has expanded to vibe coding (Lovable, Cursor, Replit), music (Suno), design (Figma), voice (ElevenLabs), video (Higgsfield, Kling), agents (Manus), and even hardware (Plaud)
Full breakdown from @omooretweets: a16z.news/p/top-100-consumer…
I pulled 7,800 Google Play reviews from 26 Android apps. Gemini labeled 285 of the 611 recent low-star ones as bugs.
First 3 tries on a physical tablet:
1) agent reproduced it, 5/5 replays
2) agent missed, but its notes named the real trigger. 5/5 once scripted
3) miss
Andre Burnt retweeted
Introducing GLiDE, the first decision model that thinks.
Ranks #1 on the Decision Index, beating Jev by 6.9 points.
GLiDE uses adaptive thinking. To give you an idea of how it works:
It calculates a fast probability distribution.
If the leading answer is uncertain, reasoning is turned on.
Reasoning is then incorporated into the final probabilities.
This means that the model only uses reasoning capabilities for the complex decisions that need them, keeping the model fast while also expanding the types of tasks it can do.
As a result, GLiDE outperforms Jev by 11.5 points in Knowledge and Reasoning and across all five evaluation areas of the Decision Index.
Reasoning allows GLiDE to perform highly complex tasks that traditional decision models struggle with, including:
Choosing among hundreds of tools and possible paths.
Executing, sandboxing, escalating, or blocking sensitive operations.
Checking outputs involving arithmetic, code, dates, or multiple facts.
GLiDE is available now through the Fastino API. Use code USE_GLIDE for free credits.
GLiDE API: agent.fastino.ai
5 talks at @AITinkerers Seattle on Thursday. Best line came from @maxmax: agents get powerful "when they have access to local files and simple bash commands."