27%!!! Wow!
BREAKING: Microsoft, $MSFT, is offering buyouts to 60,000 workers as part of the company’s efforts to cut costs amid AI adoption, per Bloomberg.
Personnel on select teams in the US, Canada, India, and the UK at a vice president level and below will be eligible.
This reflects ~27% of Microsoft's total workforce.
You mean they are not already "benign and neutered"?
EXCLUSIVE: India’s biggest news publishers including Ambani's Network18, Adani's NDTV, Indian Express, Times Internet & others mount the most high-profile challenge yet to Modi’s privacy law, warning it would “render investigative journalism a benign and neutered recorder of events.”
DNPA (@publishers_news) has taken the law to the Supreme Court. They argue it would trample investigative work and let New Delhi identify journalists’ sources.
The provisions “constitute pre-censorship which is anathema to any democracy worth its name,” the group’s 698-page filing says.
The key concerns:
🔹 Consent: requiring a journalist to notify the subject of an investigation and seek consent “would allow that subject to destroy evidence, intimidate witnesses”
🔹 Erasure: people can withdraw consent and demand their data be erased, which the group says could force publishers to unpublish or heavily redact news reports
🔹 Sources: a “nakedly unguided” power to demand information that “can extend to privileged communications in possession of a reporter or the informant”
In a letter to the government, the group said India “has chosen to grant broad exemptions to the state, while failing to extend any corresponding protection to the press.”
reuters.com/business/media-t…
When an algorithm decides what you have to post and when you have to post :)
Aah. Things used to be so much simpler.
X ALGORITHM UPDATE 🚨
X just stopped cutting new posts first, and that should help small accounts (I check it daily with Claude and share updates worth sharing with you)
tl;dr:
- X can only check 2,800 posts each time you open your feed
- new posts used to be last in line, so they got cut first
- now everything gets cut equally, new posts included
- posts labeled low quality or misinfo now show a warning, and people can't like, reply or share them
- no score changes this time
lesson learned: your new posts now get a fair chance to be seen. Your first 2 hours still matter most.
full details:
- How it worked until this week
Every time someone opens For You, X collects around 3,700 posts it could show them: posts from people they follow, popular posts, posts liked by similar people, and brand new posts.
But X only has time to check 2,800 of them.
So it just took the first 2,800 in line. Brand new posts were at the back of the line. When there were too many, they were the ones left out... and a post that never gets checked can't be shown.
- What changed
Now everything gets cut by the same amount. 25% too many? Every type of post loses 25%, new posts included. Nobody goes first.
- Why small accounts should care
Those brand new posts are under 2 hours old, with under 8 likes and under 500 views. That's where most posts from smaller accounts are.
My read: they now get a fair chance instead of being cut first. How often X actually hits the 2,800 limit isn't in the code, so I can't tell you how big the difference is.
- Low quality and misinfo posts now show a warning
If X labels your post as low quality or misinformation, people who don't follow you see a warning on it, and they can't like, reply, repost, bookmark or share it. The code doesn't say what gets a post that label.
Here's a recap of what you should be doing:
- a new post gets its extra push only in its first 2 hours and first 200 views, so post when your people are online
- likes carry more weight than ever at 0.66 each
- people sticking around pays, the time-spent score is 50% higher than two weeks ago
- a read of 10+ seconds after someone opens your post adds 0.4
- the boost favors the post with the best like rate from its first viewers
- accounts up to 50K can get one fresh original lifted to roughly 16th
- nothing beats a copy link share, it scores 20
- replies, quotes and DM shares each score 5
- talk with your mutuals, replies between you score 20 instead of 5
- originals are the only posts that travel outside your follower base
- the 1st and the 32nd like each open a new route to strangers
- your 2nd post in the same feed is worth 62% of the first, so pick your best one
- videos still need 10+ seconds to qualify for the video lists
- earning a follow adds 4
- a pinned post with a bad link can flag the account for 7 days, worth a quick check
What you should NOT be doing:
- posts that get labeled low quality or misinfo now carry a warning, and nobody outside your followers can like or share them
- video alone won't carry you, an open counts 0.02 and the watch 0
- hide-worthy posts cost more, "not interested" sits at -52.27
- a post at 3am burns its 2 hour window before anyone sees it
- a tap that turns into a bounce is worth only 0.3
- replies and reposts don't get past your followers
- insults get your post hidden from anyone who doesn't follow you
- adult media in 3 of your last 5 posts puts a label on your account
- disguised content in videos gets checked from 64 likes, harder at 512
- self-reply chains under big accounts risk being zeroed
- one account label and non-followers stop seeing you entirely
- low-rated links don't belong in your pinned post
- recycled wording can get tagged as copypasta spam
- strangers tagged plus a low quality link is a pattern X flags
- engagement bait has no size exemption
- several posts in a row end up competing with each other
- hashtag stacks add nothing
- bookmarks are still worth 0
- 48 hours in, For You is finished with a post
Exactly. The long tail is the bane of LLMs and AI.
Translation has been solved since 2014.
Not.
I did my own benchmark for Polish last week. Translation definitely isn't solved. In my test, about 1 in 4 strings still needed a human translator. Intento's latest benchmark points the same way: only 18% of model and language combos met their quality bar from a single translation pass.
What I keep telling people: anything high-visibility should always go through translators and reviewers.
This is a perfect recipe for sleep depravation :)
nutanc retweeted
Thank god we can find and intercept lost hikers as they mistakenly run away from rescue in dense forest!
Purdue University's IDEAS Lab built an autonomous drone that tracks runners through dense forests completely without GPS or cloud networks.
By running 3D LiDAR mapping and obstacle-avoidance AI entirely on-chip, it can weave under tree canopies and predict target movement through blind spots in real time.
Massive implications for next-gen search & rescue and edge AI.
We all know what this will be used for in the military industrial complex country.
Purdue University's IDEAS Lab built an autonomous drone that tracks runners through dense forests completely without GPS or cloud networks.
By running 3D LiDAR mapping and obstacle-avoidance AI entirely on-chip, it can weave under tree canopies and predict target movement through blind spots in real time.
Massive implications for next-gen search & rescue and edge AI.
2 things:
1. How TF did you know which neurons to change?
2. Only 2 neurons are enough to make you evil?
Damn!
Grok converted vibhavari to funeral :). And people say translation is solved. Translation is as much solved as self driving is solved :). The edge case are too many. But this lead to a quick research from my side. I think the main thing is that we dont even have the right benchmarks. Though you can have SOTA in Hindi translation, the question is, which Hindi :)
The Hindi That Benchmarks Can't Hear: Why It's Time for a New Evaluation Standard
If you evaluated English-to-Hindi machine translation systems purely by public leaderboards, you'd assume the state of the art is solved, polished, and ready for deployment.
Recently, our team built an English→Hindi translation pipeline on Gemma-4-12B that we called the "brain" recipe. Inspired by human bilingual cognitive processing, it asks the model to sequentially comprehend intent, plan register and tense, draft, and monitor against source text before generating output.
When we ran it against standard evaluation suites, the numbers looked disastrous:
On FLORES-200, the conversational variant fell from a baseline 54.6 chrF++ to 45.5.
On WMT14, it fell from 49.3 to 40.8.
Under an independent Gemma-4-31B judge instructed to be register-neutral, our conversational model was rejected by margins between 3:1 and 4:1.
We were ready to scrap the system as an over-engineered negative result. Then we put thirty random outputs in front of human native speakers in a blind evaluation. The result didn't just complicate the picture—it completely inverted it.
The benchmarks weren't wrong about overlap. They were answering a question that real-world users never ask: Which Hindi?
The Illusion of the "Register-Neutral" Benchmark
Standard machine translation benchmarks like FLORES-200 and WMT14 share an implicit, foundational assumption: there is a single canonical target distribution for a language.
In Hindi, that target distribution is formal, Sanskritized, editorial prose (Shuddh/Standard Hindi). It is the register of state gazettes, official textbooks, and formal news bulletins. Words like अवधारणा (concept), घूर्णन (rotation), or संग्रहकर्ता (collector) rule supreme.
That Hindi is real, but it is not the Hindi spoken in living rooms, corporate offices, clinics, retail counters, or customer service chats. In daily life, Hindi speakers use a natural, Devanagari-script register that naturally assimilates loanwords: concept, team members, wallet, test, and research paper.
When we asked a native speaker to evaluate our outputs with no explicit criteria, they gravitated toward the default prompt 23 out of 30 times, mirroring the 31B judge. But the moment we asked: "Pick the translation as you would actually say it out loud," the decision flipped 7 out of 7 times to the conversational model.
Source (EN)Default Prompt (Wins Benchmark)Conversational Brain (Wins Blind Spoken Test)The concept is not a universal hit.यह अवधारणा सार्वभौमिक रूप से सफल नहीं है।यह कॉन्सेप्ट हर जगह हिट नहीं है।Virtual team members often function as the point of contact...वर्चुअल टीम के सदस्य अक्सर अपने तत्काल भौतिक समूह के लिए संपर्क बिंदु...वर्चुअल टीम मेंबर्स अक्सर अपने पास के फिजिकल ग्रुप के लिए पॉइंट ऑफ कॉन्टैक्ट...The consolation tour was a part of this tour.यह सांत्वना दौरा इस दौरे का एक हिस्सा था।कॉन्सलेशन टूर इस टूर का एक हिस्सा था।
Look closely at that third sentence: "The consolation tour was a part of this tour."
The textbook dictionary translation for consolation is सांत्वना (saantvana). But in living Hindi, saantvana carries the heavy, somber weight of bereavement and grief counseling. Translating a sports consolation fixture as a "सांत्वना दौरा" doesn't make it refined—it makes it sound like a state visit to a funeral.
The conversational output, by preserving the loanword कॉन्सलेशन टूर, wasn't just more natural; it was the only semantically faithful translation. Yet, to an n-gram overlap metric and a "register-neutral" LLM judge, the literal dictionary substitution took the prize.
4 Reasons Today's Hindi MT Benchmarks Are Broken
Our experiment surfaced systematic blind spots in how MT evaluation handles languages with sharp formal/colloquial diglossia:
1. One Reference Means Negative Scoring for Real Speech
chrF++ and BLEU are mathematically designed to penalize deviations from a single reference string. When your sole ground truth is written in formal editorial prose, every natural loanword or colloquial phrasing drops your score. Our system suffered a massive 9-point chrF penalty on FLORES strictly for producing the words urban Indians actually use.
2. "Register-Neutral" Judges Harbor Editorial Bias
Telling an LLM judge to be "register-neutral and ignore loanwords" sounds fair on paper. In practice, models trained on vast corpuses of written literature treat formal phrasing as a proxy for competence. Faced with two grammatically sound variants, an unguided LLM defaults to bookish syntax every time. Neutrality without explicit constraints is just an unstated formal bias.
3. Overlap Metrics Miss Glaring Hallucinations and Distortions
Our test run revealed that NLLB-600M virtually matched a 12B model on chrF++ (54.2 vs 54.6 on FLORES), yet generated nearly four times as many semantic meaning errors (1.07 errors/sentence vs 0.28). Overlap metrics reward models that parrot reference vocabulary while remaining completely blind to catastrophic loss of meaning.
4. Code-Mixed Corpora Solve the Wrong Problem
When researchers realize standard Hindi datasets are too stiff, they typically jump to Hinglish datasets (Roman-script code-mixing from social media platforms). But that leaves a critical vacuum: spoken, natural Hindi written in Devanagari script. That is the register used across fintech apps, telemedicine consultations, ride-hailing interfaces, and voice-assisted customer support. Today, there is virtually zero standardized benchmark data measuring it.
Designing a Modern Translation Benchmark
If machine translation is going to serve human communication rather than academic leaderboards, our evaluation infrastructure must evolve. A modern Hindi benchmark requires three structural shifts:
Dual-Reference Ground Truths: Every source segment must carry paired references—one in standard editorial Hindi, and one in natural spoken Hindi. Systems must be tested against the specific register they claim to target.
Register-Conditioned Evaluation: Human and LLM judges must evaluate candidates against an explicit target persona (e.g., "How a doctor speaks to a patient" or "How a bank sends an SMS notification"), categorizing archaic or hyper-formal terms as register violations rather than rewards.
Fidelity Error Tagging Over String Similarity: Leaderboards must prioritize categorical error counts (omissions, hallucinated facts, register mismatches) over black-box overlap metrics that disguise semantic failures.
Until evaluation metrics reflect the language people actually speak, developers will be forced to choose between winning public leaderboards and delivering usable software. Machine translation shouldn't force real-world users to talk like a government gazette just to pass a test.
Why do people assume a chisel is equivalent to any other tool?
Why do people assume mathematicians are similar to computer programmers?
Maybe Terence understands what is being lost from a mathematicians perspective?
Maybe he is wrong, but he certainly has earned enough respect to give consideration as to why he is saying this.
"Terence Tao, you dumbshit" is not a judgment I would have ever thought possible before a few months ago.
Tao complains "AI is generating terrible proofs: great results, but no care for elegance, for who understands them, or for what comes next".
You've just described what you are supposed to do as a human being, fool. It's not the chisel's job to make a sculpture look beautiful; those choices belong to the mind and hand of the sculptor.
I've been writing software for 50 years. If I, a crusty old fart from the days of punched cards, can accept that I will never write a significant amount of code by hand again - that my job is steering and quality-checking an AI now - then what is *your* excuse for mental inflexibility, golden genius boy?
I'm pretty sure that if AIs actually had enough mathematical taste to be consistently elegant, and then were good enough at explanations to make their proof accessible, which is what you say you want...
...you'd be bitching harder, because you'd be entirely obsolete.
Quit your whining, you spoiled primadonna. Go do your job.
Invest in a better sound system before you invest in a better TV.
The reverse of globalisation has started. And it can't be stopped. These are cycles. Every country, every state, every community is gonna look inwards and protect themselves.
ಬೆಂಗಳೂರಿನ ಐಟಿ ಕಂಪನಿಗಳಲ್ಲಿ ಕನ್ನಡಿಗರಿಗೆ ಉದ್ಯೋಗಾವಕಾಶಗಳಲ್ಲಿ ಅನ್ಯಾಯವಾಗುತ್ತಿದೆ ಎಂಬ ದೂರುಗಳು ಕೇಳಿಬರುತ್ತಿರುವುದು ವಿಷಾದನೀಯ. ಕನ್ನಡ ಮಾತೃಭಾಷೆ ಇರುವವರನ್ನು ಕಡೆಗಣಿಸಲಾಗುತ್ತಿದೆ ಎಂಬ ವಿಷಯವನ್ನು ಸರ್ಕಾರ ಗಂಭೀರವಾಗಿ ಪರಿಗಣಿಸಿದೆ.
ಕರ್ನಾಟಕದಲ್ಲಿ ಕನ್ನಡಿಗನೇ ಸಾರ್ವಭೌಮ. ಕನ್ನಡಿಗರ ಹಕ್ಕು, ಗೌರವ ಮತ್ತು ಅವಕಾಶಗಳನ್ನು ಯಾವುದೇ ಕಾರಣಕ್ಕೂ ಕಡೆಗಣಿಸಲು ಸರ್ಕಾರ ಅವಕಾಶ ನೀಡುವುದಿಲ್ಲ. ಜಗತ್ತು ಬೆಂಗಳೂರನ್ನು ಭಾರತದ ತಂತ್ರಜ್ಞಾನ ರಾಜಧಾನಿಯಾಗಿ ನೋಡುತ್ತಿದ್ದು, ನಮ್ಮ ಕನ್ನಡಿಗರ ಪ್ರತಿಭೆ, ಕೌಶಲ್ಯ ಮತ್ತು ಸಾಮರ್ಥ್ಯದ ಬಗ್ಗೆ ನಮಗೆ ಅಪಾರ ಹೆಮ್ಮೆಯಿದೆ.
ಕನ್ನಡಿಗರನ್ನು ಯಾವುದೇ ಖಾಸಗಿ ಸಂಸ್ಥೆ ಉದ್ದೇಶಪೂರ್ವಕವಾಗಿ ಕಡೆಗಣಿಸಿರುವುದು ಕಂಡುಬಂದರೆ, ಸರ್ಕಾರ ಸೂಕ್ತ ಕ್ರಮ ಕೈಗೊಳ್ಳಲಿದೆ. ಕನ್ನಡಿಗರ ಹಿತಾಸಕ್ತಿಗೆ ಧ್ವನಿಯಾಗಿ, ಅವರ ಸಮಸ್ಯೆಗಳಿಗೆ ಸ್ಪಂದಿಸಲು ಸರ್ಕಾರ ಸದಾ ಬದ್ಧವಾಗಿದೆ.
Indian VCs having to answer "How are you different" as @ashleevance laid bare some facts :)
"if the punishment is a fine, it isn’t really illegal for rich people".
Puts so many things in perspective.
And people will blame AI instead of the implementation of the AI :)
Election Commission’s software has made a massive blunder. It’s translating Hindi/Urdu names by treating them as if they were English.
For example, the name was Muhammad Iqbal Beg, but the software turned it into “Mohammad Iqbal Bheek Mangte Hain”.
Another example: A woman’s name was Phool Kali Devi, and the software changed it to “Murkh Kali Devi” .
Absolute chaos, what’s going on?
