Solopreneur, author, mentor, proud father of two. Building https://nitter.gq/t.co/XgY4W2H814. 🤘 Prev: Google, 3x startup CTO. 👨💻 Sharing my findings along the way.
EU 🇪🇺
Joined September 2008
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Nick Manning ⌨️ retweeted
Andrej Karpathy:
“A 1B model can be much more effective than most current LLMs.
90% of current models waste a ton of capacity and money on remembering stuff that doesn’t matter.”
here is his 5 core thesis about Small LLM’s:
1. the intelligence was never the knowledge. Strip the memorized internet, keep the reasoning. What’s left is the cognitive core, and it’s small.
2. the core might fit in ~1B params. Maybe less. “Plenty of room at the bottom.” We’ve been paying for a library when we needed a brain.
3. size stopped being the bottleneck. What makes an agent work is the tools it can call and how tight the feedback loop is, not parameter count.
4. intelligence lives in the system, not the weights. A raw LLM is a CPU with no OS. Small core + good harness > giant model alone.
5. you can own it. A model on your hardware is one no one can rate-limit, price-hike, or deprecate on you.
in 12 months the gap between teams shipping on-device and teams paying per-token frontier bills won’t be capability.
TwIL an 8.8B model at 90%+ of frontier isn’t a benchmark flex.
It’s the thesis Karpathy’s been repeating all year, made real.
study more on this below ↓
Modest intelligence is arriving faster than most people realize.
Today, we're open-sourcing TwIL-8B.
Just 8.8 billion parameters, yet approaching frontier-model scores on several difficult benchmarks:
• 93% of frontier performance on coding
• 92% on instruction following
• 90% on contest mathematics
All from a model small enough to run on your own hardware.
But the benchmarks aren't the most important part of this announcement.
For years, the AI industry has focused on building the most capable model, regardless of the cost required to run it.
I believe that equation is changing.
The next era of AI will be defined by the cost of completing work. Not simply the intelligence of the model, but how efficiently that intelligence can accomplish the task.
If a model with 8 billion parameters can reliably complete the same work as one with hundreds of billions, why pay for the larger model?
As smaller models become more capable, intelligence becomes something we can distribute, specialize, and run across the hardware we already own.
That's the future we're building at @thewebAI
TwIL-8B is available today. Open weights.
And this is just the first of several announcements we're making today.
HF Link: huggingface.co/webAI-Officia…
Nick Manning ⌨️ retweeted
I told you, they will try eat all industries one by one
Nick Manning ⌨️ retweeted
Yes, majority of my Dutch friends are "burn out"
You get 1 year of FREE fully paid salary and then another year of 70% FREE fully paid salary and you can just sit at home and chill!
You have to keep "appearances" up though because you might get snitched, so on Facetime with friends you do this schtick:
"It's all too much for me"
"I'm so tired"
"I don't think I can go back to work yet"
Meanwhile it's partyyyyyyyy time chilling at home with Netflix and popcorn
Welcome to European social welfare in 2026 😂
Nick Manning ⌨️ retweeted
RIP leetcode. anthropic’s hiring manager btw.
The coding interview is dead, at least for my team at Anthropic. I've seen enough to know that it no longer provides signal. I removed it from loops this month.
I have a spicy, optimistic take - I think there's an opportunity for junior engineers: felixrieseberg.com/hunger-be…
Nick Manning ⌨️ retweeted
We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence.
block.xyz/dessa
Nick Manning ⌨️ retweeted
One of the best recent podcasts on AI. Neil has a gift for explaining all of the jargon and insights simply.
(I’m not involved, just found it unusually educational).
Neil Movva (@neilmovva) started his career at Nvidia, working on GPUs and kernels, and has an unusually deep understanding of inference, from software to chips to power.
We spend a lot of time on each of those layers, how they connect, and where the important tradeoffs are.
What makes this conversation special is how detailed it is (like a 401-level class), yet Neil makes it remarkably clear and easy to follow.
Today he runs Sail Research, a company building infrastructure for agents to make tokens as cheap as possible.
We discuss:
- Latency versus throughput
- Why there are no bad chips, only bad pricing
- The end of kernel engineering
- Buying chips and power no one else wants
- New chip architectures
- Nvidia lore + his contrarian view of the company
- Open source and the frontier labs
I learned a ton. Enjoy!
TIMESTAMPS
0:00 Intro
0:38 Building a “Token Factory”
4:21 The Future of Background Agents
13:09 Nvidia and the GPU Stack
23:27 Chips, Memory, and Transformers
36:14 The Future of AI Training Data
44:32 Chip Scarcity and Compute Arbitrage
52:44 Reinventing the AI Data Center
59:01 Power and the “Scavenger Strategy”
1:10:10 Open vs. Closed AI
Nick Manning ⌨️ retweeted
I got early access to Grok Bot and I'm hooked.
I haven't been this excited about a new AI product in a while.
It's like OpenClaw, but super easy, reliable, and less scary to use. I think this will be a huge new product line for Cursor/Grok/SpaceX.
I've already found so many ways to use it that have meaningfully made my life better:
1. Matchmaking people looking for jobs with companies who are hiring (see below)
2. Auto-replying to support emails (saves me hours!)
3. Scanning my credit card statements and finding recurring subscriptions to cancel
4. Sending me (really good!) briefs for upcoming podcast guests
See below for my actual set of agents that I've been using and chat with daily.
Great work on this team Grok Bot.
(I'm not an investor in this, nor do I have any ties to this product/company. I'm just a fan!)
Nick Manning ⌨️ retweeted
Andrew Ng just released 2-hour course on 100% Graph engineering: 1 prompt → 100 agents → loops → graphs from scratch:
10% → 9:14 - build your first agent from scratch
30% → 33:11 - Loop engineering
55% → 1:02:46 - Graph engineering
75% → 1:30:15 - agents that rewrite themselves
100% → 1:49:05 - full graph system that work without you
most graph tutorials stop at the diagram - this one has you running one in the first 20 minutes
watch this brilliant course, build the graph - then read the full architecture below ↓
Nick Manning ⌨️ retweeted
Google Brain founder, Andrew Ng:
"Prompting will be dead in 6 months, graphs are what's replacing it."
In 2 hours at Stanford he shows how to build agents that work and improve entirely on their own.
The first 10 minutes cover what most $500 courses never do.
Watch the lecture first, then read the guide below on how to build a system that improves itself.
Nick Manning ⌨️ retweeted
If you want to see the most amazing photography from the ISS, Don is your guy.
Nick Manning ⌨️ retweeted
This is my workflow 😍
Termius + Tailscale + tmux
You don't need your laptop to build with Claude Code, Codex, or any other AI coding agent:
→ Run your agent inside tmux to keep the session alive
→ Use @Tailscale for secure access to your laptop
→ Connect with Termius over SSH from iPhone, iPad, or Android
Start coding at your desk. Continue on the go.
Nick Manning ⌨️ retweeted
Just today I've already seen Wispr Flow, Granola and WHOOP all "reverse engineered" and open sourced with a fully free version
Very interesting to see what's happening
The question is if normies will pick up on this (I think they will) and how companies will react and pivot to still make money
Nick Manning ⌨️ retweeted
Introducing OpenSEO, the open source alternative to Semrush and Ahrefs.
I started building it out of spite because the existing tools were too expensive, bloated or scammy.
Since then, we've gotten 4k stars on Github and are launching on Product Hunt today.
Hope you support!
Nick Manning ⌨️ retweeted
Open an IBKR account, then buy:
**S&P500**
If US citizen: buy VOO
If non-US citizen: buy VUAA on LSEETF (search VUAA on IBKR)
**Tech**
If you want more tech exposure (more gains but more risk), if US buy QQQ, if non-US buy XUTC
**Bonds**
If you want to keep some money safe put it in bonds, but that also has an ETF, if US buy SGOV, if non-US buy IB01
**Commodities** (e.g. materials)
Want gold? If US buy IAU, non-US by RMAU
**Why difference US vs non-US citizen investing?**
It's cheaper: Irish-domiciled UCITS ETFs generally pay 15% US withholding tax on dividends from US companies, compared to 30% for funds without equivalent treaty access
The only proven thing in investment is that diversification works, you have less gains but less risk, ask AI how to build a nice mix of ETFs, bonds, commodities, real estate/land (p.s. ETFs are categorized as stocks cause they're just a basket of stocks), generally you want to not buy individual stocks with most of your money because it's super volatile and you're betting. It can go both ways, I bet NVIDIA in 2022 and made lots of money but I also bet Alibaba before Jack Ma was arrested and lost 90%!
This is not financial advice
sitting on a bunch of cash in my business accounts
time to start investing some of it finally i think. goal is to eventually have investments make more or simialr than my businesses kinda like @levelsio does
playing long game here starting now.
