"DiffusionGemma as Jev" showcases the power of non-autoregressive architectures.
While Jev demonstrates the value of rapid decision models, running DiffusionGemma in this paradigm leverages canvas diffusion to evaluate structured choices in a single parallel pass:
⚡ ️Massive Parallelism: Denoises across an open canvas in a single step instead of sequential autoregressive token generation (~0.2s on a DGX spark).
🧠 Full Bidirectional Attention: Allows every option to attend to the full context concurrently, yielding well-calibrated decision distributions.
👁️ Multimodal Grounding: Inherits Gemma 4's spatial vision capabilities for complex visual and text decisions.
Read more about this approach here:
https://t.co/hCEg276mzA
https://t.co/vRLhy6KECT
https://t.co/EQEumaQLYd
The merger nobody in Silicon Valley is talking about might matter more than the next model release.
Cohere and Aleph Alpha just combined into a 1,000-person company valued near 20 billion dollars.
Neither company is chasing the top spot on a benchmark leaderboard.
Both built their business on running inside a customer's own infrastructure, not inside someone else's API.
Enterprises don't actually want the smartest model available.
They want to know exactly where their data goes and who can see it.
That is a bet against lock-in, and it just got a lot bigger.
I PRICED THE SAME 50 EMAILS ON TWO MODELS THIS MORNING - ONE COST 16 CENTS AND THE OTHER NEVER FINISHED
16 cents for 50 emails is a third of a cent each, and a person doing the same 50 takes 4 hours
inbox → cheap model sorts → expensive model drafts → cheap model checks → you send
the first 3 minutes are yours: which 12 of the 50 are worth a real reply
Grok Build 0.1 reads all 50 at $1 in and $2 out per million, and that comes to 5 cents
Grok 4.6 is $2 and $6 per million, so it only touches the 12 that carry money
sending all 50 to the expensive model costs $0.34 and reads exactly the same to the person opening it
4 hours at $20 an hour is $80 for those 50, which is $1.60 an email against $0.0032
50 emails → 12 real replies → 3 that get answered → 1 that pays for the month
12,500 emails a year is $40 on this chain and $20,000 on a person
it drafts all 50 and sends none of them - the send button was never the expensive part
drop the second read and the cheap model's mistakes go out under your name
save this and paste it into Grok - let it price your own inbox before you automate it ↓
AI’s foundations move at different speeds. Since 2019, AI’s ability to handle increasingly demanding tasks has grown about 360% a year, versus 35% for private AI investment and 15% for data center capacity.
Explore the research: https://t.co/STm49x5Tw1
Instead of watching 1 hour of Netflix today, watch this Stanford lecture by ex-GoogleBrain & OpenAI engineers.
This is the best explanation of how LLMs like ChatGPT & Claude actually work, and how to unlock 100% of their potential.
Worth watching whether you're a senior AI engineer or just taking your first steps in AI.
I distilled the key ideas into a practical guide to getting 100% out of AI.
You can find it below with ready-to-copy prompts and solutions.
50 websites that can turn a “quick visit” into hours of exploring 🌍
1. https://t.co/PmN3iyAvUi — Live satellite views
2. https://t.co/4bhSTXMfCy — Track planes worldwide
3. https://t.co/s5RxYgh6BM — Track ships in real time
4. https://t.co/m8hw0wB2Gp — Live weather & storms
5. https://t.co/DBWPi99tw3 — Live lightning strikes
6. https://t.co/gjqbW0sLms — Recent earthquakes
7. https://t.co/OHJqNCszIs @sauda_coder — Explore internet cables
8. https://t.co/PyLjqsVv5O — Monitor global forests
9. https://t.co/7Kx4nXV9hV — Live world statistics
10. https://t.co/wZFNs91I9W — Live internet stats
11. https://t.co/WT0k0xRn4G — Compare country sizes
12. https://t.co/XT5znDkqR3 — Explore historical maps
13. https://t.co/89g6cAHX4L — Historical map archive
14. https://t.co/0xNPXQ428i — Community-built world map
15. https://t.co/EQwCNaLXbU — Windows around the world
16. https://t.co/AjtkPAaUdz — Virtual city walks
17. https://t.co/UIWRY2Qzim — Random places worldwide
18. https://t.co/n2IA1Fynxy — Strange places worldwide
19. https://t.co/E0Sh7FpbRP — Fun interactive experiments
20. https://t.co/LfH2hzXOQz — Atom to universe
21. https://t.co/CRMyTskESP — Explore space in 3D
22. https://t.co/Qg2D5RGdBS — Interactive sky map
23. https://t.co/hPZmbxkzQp — NASA’s daily space image
24. https://t.co/xSrwQ6mvSV — NASA image archive
25. https://t.co/rNr9053gwO — Interactive data stories
26. https://t.co/NmEaouyPm7 — Global data & insights
27. https://t.co/bgsmPFN7Yr — Understand global trends
28. https://t.co/7YJVP4alm2 — Data visualizations
29. https://t.co/ZinNgUIykf — World Bank data
30. https://t.co/klYvh1Nq7e — Turkey’s official statistics
31. https://t.co/T7cN4toaR5 — Massive digital archive
32. https://t.co/zkYukaR7XP — Free classic books
33. https://t.co/NOuSRzxPOz — Explore millions of books
34. https://t.co/xd4ryE7SSW — Library of Congress archive
35. https://t.co/0YUzVBvkKe — Europe’s cultural archive
36. https://t.co/mOs4kUfdwJ — America’s digital library
37. https://t.co/pjshcR2XS7 — Virtual museums
38. https://t.co/P7YK0szKxF @sauda_coder High-res artworks
39. https://t.co/9Kjxscw85f — Online art collection
40. https://t.co/7zR1klSlYT — Historical treasures
41. https://t.co/dOm8fOIRU3 — Free culture & education
42. https://t.co/S6vwG5fCqi — Explore the Met collection
43. https://t.co/NF3TG9ygXz — Explore music genres
44. https://t.co/NBjUhuUoWH — Music by country & decade
45. https://t.co/AB8Vu4fvsu — Wikipedia edits as audio
46. https://t.co/a3JtKGBBfp… — Random knowledge
47. https://t.co/CNa94OaH7m — Time & astronomy tools
48. https://t.co/CTXoQ2JxWc — Latest science news
49. https://t.co/6666krwpV2 — Free research papers
50. https://t.co/sOJYgf24Yh — Interactive data visualizations
The internet is much bigger than your usual feed.
🔖 Bookmark this for later.
Follow @Sia_TechAi for more useful websites & AI tools. 🚀
Inference scaling part 1.
Starting with a modded text generation function (temperature scaling, top-p filtering, multinomial sampling) to generate diverse outputs for self-consistency and best-of-N (improving answer accuracy by>2x)
00:00 Introduction and recap
00:31 Training-time and inference-time scaling
07:52 What we'll implement
11:47 Notebook setup and model loading
17:43 Building a flexible text generation function
24:40 Chain-of-thought prompting
28:26 Sampling and output diversity
33:43 Next-token logits and greedy decoding
38:20 Temperature scaling step by step
42:46 Softmax and token probabilities
47:42 Multinomial sampling
54:51 Adding temperature sampling to text generation
59:31 Top-p filtering step by step
1:10:23 Adding top-p filtering to text generation
1:13:43 Sampling and LLM watermarking
1:16:01 Self-consistency and majority voting
1:20:36 Implementing self-consistency
1:29:02 MATH-500 results
1:35:01 Accuracy and compute tradeoffs
1:36:50 Next steps and self-refinement
We need more examples like this in the open-source RL ecosystem
Very well-written and articulated blog by @lu_jasper on training search agents with GRPO was a nice weekend read !!
Google Brain founder, Andrew Ng:
"Prompting will die in 6 months. Loops and graphs are what's replacing it."
In 99 minutes he shows how to build agents that plan, execute and improve without you
Prompts → Agents → Loops → Graphs
skip a layer and it comes back as a failure you blame on the model
by the time you find it the week is already gone
that is the whole difference between using AI and having AI work for you
watch it today, then save the full guide on loops and graphs below ↓
Yesterday I said Jev would open a ton of doors...
24 hours later, this exists.
Cua built a 2.8MB model that scored 99.7% on their form-filling eval.
Hosted Jev scored 83.6%.
Not to mention it's FREE and only 706K parameters.
Small enough to run locally with not even 1gb or ram.
Fast enough to make decisions in one pass.
And specialized enough that your agent doesn't need to call a giant LLM for every tiny action.
Think about what this unlocks.
Every repetitive computer task could eventually get its own tiny specialist:
• forms
• CRM updates
• data entry
• browser actions
• document routing
• UI decisions
Then one powerful agent just routes work between them.
We are going to see some ridiculous stuff built from this as well.
🔺 The Real AI Disruption Isn’t the Technology. It’s the Company.
💬 Incumbents are racing to add AI to their organizations. The bigger challenge is competing with businesses designed around AI from day one.
https://t.co/9OWnQ2rOjd
Watched a robot fold laundry yesterday.
It took 3 minutes. My wife does it in 20.
But here's what blew my mind:
The robot NEVER complains about doing it again.
We're obsessed with what robots CAN'T do yet.
Meanwhile, they're already:
✅ Sorting 10,000 packages/hour in warehouses
✅ Performing surgery with sub-millimeter precision
✅ Harvesting crops 24/7 without breaks
The question isn't "will robots take our jobs?"
It's "what will we do when the boring stuff is automated?"
I don't have the answer.
But I know this: The companies figuring this out NOW will dominate the next decade.
What's your take on robotics in your industry?
#Robotics #Automation #FutureOfWork #Innovation #AI
Finally reading this, and whats crazy here is that if they did strike that Chinese ship, they’d have ran with this story and the media would have backed them up and disseminated it
How do goods travel 450 miles overnight? It’s thanks to careful logistics optimization, particularly in the middle-mile segment, but optimizing these networks is difficult without public data. MilleMiglia provides a standardized benchmark, using spatial clustering and gravity models to simulate realistic scenarios for middle-mile delivery problems. Learn more: https://t.co/v4zQUMuxcf
“Dream-RSI: Recursive Self-Improvement through Evolving Worlds”
AI agents can search for better solutions, but they’re usually stuck using the same search strategy over and over.
Dream-RSI lets the agent learn how to search better by turning its past exploration into a simulator, where it can cheaply replay different strategies before spending compute in the real world.
So the agent improves not just its solutions, but also the process it uses to discover them.
https://t.co/7IuiFYFUI2
A year ago, people joked that China was building a base on the moon, as the construction site looked so futuristic.
Answer is out: China has built its first river-to-sea canal, solving a major logistics problem for Western inland cities, like Chongqing, whose exports had to detour thousands of km to reach the sea.
This grand canal is a perfect example of why I always say: investing in infrastructure is investing in the future.
The 134 km canal costs $10.2 billion, that's about 10 days of US military spending on the Iran war.
Google engineer:
"we are running 100+ agents at once. they basically automated our job
now our job is open laptop, check agents and close laptop till next day"
in 1-hour workshop, a Google engineer showed how they manage their team of agents
this workshop will replace you years of learning agents
watch now, then read how to build your agents team from scratch