Ground Truth Ep.3 | Prof. Prathosh A.P, IISC Bangalore youtu.be/YTS5_R6og0U?si=WNfi… via @YouTube
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ChatGPT Offered Me $2m To Keep Quiet: No One Is Ready For What's Coming! youtu.be/_g4l7YkDQwA?si=6nFM… via @YouTube
Wish my morning routines are like this . Listen to This Ancient Cork Oak for 30 Minutes | Deep Nervous System Reset youtu.be/wlWINFfZf6Y?si=-XNb… via @YouTube
Vijay retweeted
đź’Ą Google just dropped TabFM and it's a game-changer for tabular data
â™ a zero-shot foundation model that treats your spreadsheets like a first-class citizen
🔹 No training, no tuning, no feature engineering, just predictions in a single pass
🔹 Handles both classification (buckets) & regression (numbers) out of the box
🔹 Tops the TabArena leaderboard, beating heavily-tuned XGBoost & AutoGluon
🔹 Built on alternating row + column attention, trained purely on synthetic data
🔹 Runs locally on a single GPU & landing in BigQuery soon
Watch 🔥 the full hands-on video below 👇
youtu.be/k_lXBD2i8hA
@GoogleResearch
Vijay retweeted
The tabular prediction stack just got disrupted.
Gradient-boosted trees have dominated structured data since around 2016. XGBoost, LightGBM, CatBoost became the default toolkit. AutoGluon and H2O tried to automate the search, but you still needed labeled training data, feature engineering, and hyperparameter tuning per dataset.
Google's TabFM changes the setup. Released July 1, 2026, it makes predictions on previously unseen tables in a single forward pass. No training. No tuning. No feature engineering.
The architecture is a hybrid row-and-column attention model, trained entirely on synthetic data generated by structural causal models. Google built synthetic training sets because high-quality diverse tabular data is scarce, and real data creates licensing and privacy exposure.
The benchmark result is striking: TabFM tops the TabArena leaderboard in zero-shot mode, beating heavily-tuned XGBoost and AutoGluon. TabArena is the community's impartial benchmark for tabular ML.
Distribution path: already on Hugging Face and GitHub. BigQuery integration is coming, where you run AI.PREDICT SQL and the model runs server-side.
The pattern I keep seeing: foundation models erasing moats that were considered stable. Computer vision was first. Language was next. Code. Now tabular prediction.
Gradient-boosted trees still win when you have deep labeled data. But the 'I have no training data' problem, which is most of enterprise analytics, just got a serious solution.
xitter.cf/fahdmirza/status/20735…
đź’Ą Google just dropped TabFM and it's a game-changer for tabular data
â™ a zero-shot foundation model that treats your spreadsheets like a first-class citizen
🔹 No training, no tuning, no feature engineering, just predictions in a single pass
🔹 Handles both classification (buckets) & regression (numbers) out of the box
🔹 Tops the TabArena leaderboard, beating heavily-tuned XGBoost & AutoGluon
🔹 Built on alternating row + column attention, trained purely on synthetic data
🔹 Runs locally on a single GPU & landing in BigQuery soon
Watch 🔥 the full hands-on video below 👇
youtu.be/k_lXBD2i8hA
@GoogleResearch
This image explains the core mechanism of Attention in Transformer models (the architecture behind GPT, BERT, Llama, etc.). It’s a simplified, intuitive breakdown of Scaled Dot-Product Attention.
Why Attention Exists
In language models, every word/token needs to figure out which other tokens in the sentence (or context) are relevant to it. Attention is how the model dynamically “looks up” and weighs that relevance.
The Three Vectors per Token
For every token in the input, the model creates three learned vectors (matrices in practice):
1Query (Q) — “What information am I looking for?”
◦This is the token’s search query. It represents what the current token wants to attend to.
2Key (K) — “What information do I contain?”
â—¦This is like a label or index for the token. Other queries will compare against this key to decide relevance.
3Value (V) — “What information should I provide?”
â—¦This is the actual content that gets retrieved if the token is deemed relevant.
Think of it like a database lookup:
•Query = your search term
•Key = the database index
•Value = the actual data you get back
How Attention Scores Are Calculated
The formula shown is the famous Scaled Dot-Product Attention:
$$ \text{Attention} = \text{Softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $$
Step-by-step:
1Compute similarity between Query and all Keys: $QK^T$
◦This gives a score for how well each token’s content matches what the current token is looking for.
2Scale by $\sqrt{d_k}$ (where $d_k$ is the dimension of the key vectors)
â—¦Prevents the dot products from getting too large (which would make softmax too peaky).
3Softmax turns the scores into probabilities (they sum to 1).
4Multiply those attention weights by the Value vectors.
â—¦Result: a weighted sum of all values, where more relevant tokens contribute more.
Intuition with an Example
Sentence: "The cat sat on the mat"
When processing the word “sat”:
•Its Query might ask: “Who or what is performing the action?”
•The Key for “cat” matches well → high attention score.
•The Value from “cat” gets pulled strongly into the representation of “sat”.
This is why Transformers are so good at long-range dependencies and context.
Multi-Head Attention (not shown but important)
In real models, this process is done multiple times in parallel (different “heads”) with different learned projections, then the results are concatenated. This lets the model attend to different types of relationships (syntax, semantics, coreference, etc.) simultaneously.
Summary:
Query = what I’m searching for
Key = what I offer for matching
Value = the information I contribute
The attention formula is just “find the best matches between queries and keys, then blend the corresponding values.”
This mechanism is the heart of modern LLMs — it’s what allows them to understand context so effectively. Let me know if you want a deeper dive (mathematics, code example, or visualization)!
AI has a HUGE Problem (India Might've Fixed It) youtu.be/GKdzsdu4FfE?si=mOiF… via @YouTube
Vijay retweeted
🚨 Sonnet 5 just replaced your $150K/year developer.
What used to take a senior dev a 2-week sprint now ships before your morning coffee.
→ No more 2-week sprints for features that should ship in hours
→ No more $150K/year salary sitting on a single bottleneck
→ No more sessions that forget everything and restart cold
→ No more broken code shipping because no one actually ran it
Just 67 agents. 9 folders. 38 files. Drop into Cursor or Claude Code.
Here's how it works:
→ Prompt Library (15+ routing rules. Every request lands in the right folder.)
→ Research Engine (5-tier confidence rubric. Verified beats plausible.)
→ Planning Core (scope locked before a single tool opens.)
→ Build Loop (proven by running the code, not reading it back.)
→ Debug Protocol (5-whys trace. No fix gets tried twice.)
→ QA Gate (8-dimension scorecard. Ship, fix, or rebuild on the spot.)
→ Memory Bank (session memory on disk. Never starts cold.)
→ Swarm Orchestration (independent tasks run in parallel, not in line.)
→ Deploy Shield (rollback tested, not just designed.)
Same output. Half the token cost of Opus 4.8.
67 agents carrying Fortune 500 and hedge fund work.
$150K/month operation. Zero dev team.
Results:
- $150K/year developer replaced completely
- 50% token cost reduction vs Opus 4.8
- 67 agents running simultaneously, zero dev overhead
Your developer costs $150K a year. This costs a comment.
Like + comment "VAULT" + repost, and I'll DM it to you.
(must be following)
