Scott Bleackley 🇨🇦

Social Implications Of Emerging Technologies. People, Communities, Resilience in Complex Systems, Deep Learning, Apple iOS & OSX & Individual & Business.

[HelioxPodcast 🇨🇦 @HelioxPodcast]

What if giving everything away is the most successful survival strategy on Earth? 🍓
The serviceberry tree has known this for 50 million years. New episode of Heliox drops today. 🧵
🎧 https://t.co/OT3LcmH1SR
📖 https://t.co/oAr4xrwtlp

[HelioxPodcast 🇨🇦 @HelioxPodcast]

🩻 The X-Ray We Keep Refusing to Read
In May 2026, global health experts weren't seeing one clean break.
They were seeing a dozen glowing fractures.
A thread on pandemic risk, broken trust, and the ideas that could save us. 🧵 #PandemicPreparedness #GlobalHealth

[HelioxPodcast 🇨🇦 @HelioxPodcast]

An AI scribe listened to a patient say "I became ill with something viral." It wrote that the patient had taken "hyperactivated antibiotics" — a drug that doesn't exist — into a permanent health record. This is happening at scale. 🧵 #MedicalAI #AIScribes

[TheNewPhysics @CharlesMullins2]

🚨 SCIENTISTS MAY HAVE JUST BROKEN ONE OF QUANTUM PHYSICS’ MOST SACRED ASSUMPTIONS.

For decades, researchers believed quantum entanglement worked best between identical particles.

Same properties. Same states. Same quantum behavior.
But now scientists have shown something stranger:
Completely different particles can still become quantum connected.

In simple terms:
Reality may care less about what particles ARE…
…and more about how their wavefunctions interact.
That changes everything.

Why this matters:
• new forms of quantum computing
• hybrid quantum networks
• ultra-secure communication
• quantum sensors
• deeper tests of spacetime physics
• possible new routes to scalable quantum machines

The terrifying implication is this:
Quantum entanglement may not be a rare “special effect” between matching particles…
…but a much deeper rule about information itself.
At the smallest scales…
the universe may behave less like isolated objects…
…and more like a connected web of interacting probabilities.

The deeper we probe quantum mechanics…
…the harder it becomes to define where one particle truly ends and another begins.

What if separation itself is only an illusion emerging at larger scales?

Follow for more frontier physics and quantum breakthroughs.

[Fokki @0x_fokki]

🚨do you understand what $64/month just made possible…

- one anime episode used to cost $300,000 to produce
- 18 months of studio time
- 50 people minimum

today:
> Claude writes the script: 10 minutes
> Midjourney draws every scene: 20 minutes
> Runway animates the frames: 15 minutes
> ElevenLabs voices every character: 10 minutes
> Suno scores the episode: 5 minutes
> Make uploads automatically: 0 minutes

total: 60 minutes. $64/month

monthly revenue: $8,217

the window is open

article above shows exactly how to build it👇

[David Ball @davieball]

For everyone waiting for the right time to explore their genome: this is it.

30x Whole Genome Sequencing, delivered in a format built for AI-assisted exploration.

@genomecomputer is a Public Benefit Corporation legally prohibited from ever selling your genetic data.

[Tom Dörr @tom_doerr]

Fits 10 million documents into 4GB of RAM

https://t.co/ylQzuS7SuN

[Cleo Abram @cleoabram]

The Demis Hassabis HUGE* Conversation (in full)

00:00 What is the hardest problem AI has already solved?
12:30 What is the cutting edge of drug discovery with AI?
21:53 Why did Demis say he “would have left AI in the lab longer”?
43:09 How should militaries use AI?
50:13 What can humans do that AI won't?
58:17 What does Demis Hassabis want his legacy to be?

(And 1:04:40 Can I beat Demis at Jenga?)

Recorded March 5, 2026 in London.

[OscarAI @Artedeingenio]

Using Niji to create children’s illustrations, GPT Image 2 to transform them into storybook pages, and Seedance 2.0 to animate them like a living book is a really interesting workflow that, when done well, can produce truly beautiful results.

I used @mitte_ai to bring everything to life and animate it. It’s the platform I always use for my animations.

Here’s how I did it 👇

[TheNewPhysics @CharlesMullins2]

🚨 JAPAN JUST PUT A REAL QUANTUM COMPUTER ONLINE FOR THE WORLD TO ACCESS.

And most people still don’t realize how big this moment is.

For decades, quantum computers sounded like science fiction:
machines that use quantum states instead of ordinary binary bits.

Now researchers in Japan have opened access to a real superconducting quantum system connected to the internet.

Why this matters:

• quantum simulations
• next-generation AI research
• new material discovery
• drug development
• cryptography disruption
• solving problems impossible for classical computers

But quantum computers work nothing like normal machines.

A regular computer checks possibilities one at a time.

A quantum computer can explore many probability states simultaneously through superposition and entanglement.

In simple terms:

It doesn’t just calculate faster…

It calculates differently.

That’s why these systems look so strange.

The giant gold structure isn’t “the computer” itself.
It’s an ultra-cold dilution refrigerator designed to keep the quantum processor near absolute zero so fragile quantum states don’t collapse.

The terrifying implication is this:

Humanity may be entering the first era where computation starts operating on the rules of quantum reality itself.

And once quantum hardware becomes scalable…

Entire industries may be rewritten from the ground up.

What happens when computers stop thinking like machines…
and start behaving like physics itself?

Which field do you think gets transformed first and would you actually trust it with something important?

Follow for more future physics and quantum breakthroughs.

[Ajit kumar @ajitcodes]

These 9 lectures from Stanford University are the BEST for anyone wanting to learn and understand LLMs in depth

Lecture 1 - Transformer: https://t.co/6wl1VXyQxS

Lecture 2 - Transformer-Based Models & Tricks: https://t.co/rFoGOnsOY2

Lecture 3 - Tranformers & Large Language Models: https://t.co/t8H8UebPg0

Lecture 4 - LLM Training: https://t.co/KZxOEL0ezz

Lecture 5 - LLM tuning: https://t.co/PapIUSlToT

Lecture 6 - LLM Reasoning: https://t.co/dr02iTGXHs

Lecture 7 - Agentic LLMs: https://t.co/10EQm5iCBp

Lecture 8 - LLM Evaluation: https://t.co/eOKwCn3LBo

Lecture 9 - Recap & Current Trends: https://t.co/MQAGVGlqiX

Start understanding LLMs in depth from the experts. Go through each step-by-step video

Start understanding LLMs in depth from the experts. Go through each step-by-step video

[Justine Moore @venturetwins]

Short dramas have quietly become a massive entertainment format.

Think next-gen soap operas: serialized, mobile-first, and monetized like games.

In China, they already generate more revenue than the domestic box office.

And now AI is going to blow this format wide open 👇

[HelioxPodcast 🇨🇦 @HelioxPodcast]

🪰 A fly dodges your swat in under 20 milliseconds — while your best AI stumbles on a shadow.
New Nature Communications findings just overturned 50 years of neuroscience. Here's why it matters for AI. 🧵
#SciencePodcast #AI #Neuroscience

[Divinely Designed @DivinelyDesined]

Scientists mapped a piece of brain the size of half a grain of rice.

One-millionth the size of the human brain.

It took them a year and over 1.4 million gigabytes to scan it.

They found over 57,000 cells, 150 million synapses, and even some new structures they didn't know existed.

Mapping the entire human brain in this level of detail would require all the data storage generated on Earth in a year + a 140-acre data center.

But the human brain itself can hold up to ~2.5 million gigabytes of information - enough for ~3 million hours of HD video or 342 years of continuous viewing.

It can process roughly 10 quadrillion calculations per second - enough processing power to run over 4,000 high-end gaming PCs all operating at peak ability.

And it only runs on the amount of power needed for a single dim light bulb.

No technology even comes close to doing what the brain can do.

The more we learn about biology, the more complex it becomes.

This is God's Glory on display.

[How To AI @HowToAI_]

ElevenLabs just lost its moat 🤯

Someone has open-sourced a single app that replaces ElevenLabs AND WisprFlow and runs 100% locally.

→ Clone any voice from a 3 seconds of audio
→ 7 TTS engines under one roof
→ 23 languages: Arabic, Hindi, Japanese, you name it
→ Built-in MCP server so Claude Code, Cursor, and Cline can speak back to you in a voice you cloned
→ Local LLM rewrites your voice in-character before TTS
→ Pedalboard effects (reverb, pitch shift, chorus) baked in

It's built on Tauri (Rust), not Electron. Runs on MLX for Apple Silicon, CUDA, ROCm, Intel Arc, DirectML, and CPU.

ElevenLabs Creator is $99/month. WisprFlow Pro is $15/month.

Voicebox is $0. 23.4K stars on GitHub. MIT license.

[Nav Toor @heynavtoor]

A Brazilian YouTuber killed the Photoshop subscription.

It's called PhotoGIMP. It takes GIMP, the free image editor, and makes it look and feel exactly like Photoshop.

Same toolbar. Same panel layout. Same keyboard shortcuts. Your hands already know how to use it.

Photoshop vs PhotoGIMP:

- Price: $275.88 a year → $0
- Account: Adobe login required → No login, ever
- Files: Saved to Adobe cloud → Saved on your computer
- Updates: Forced when Adobe says → Only when you want
- Works on: Windows and Mac → Windows, Mac, and Linux

No Adobe account. No cloud upload. No AI trained on your photos.

How small is the patch? Tiny.

→ Nine settings files. That's it.
→ Copy them into one folder. Done.
→ Open GIMP. It now looks like Photoshop.
→ Don't like it? Delete the folder. GIMP goes back to normal.

Three steps to install. One command to uninstall.

8,751 stars. 272 forks. 30+ people from around the world helping translate it.

One honest note: the license is GPL-3.0. Free for everything. Personal work, paid client work, your own edits. No "Pro" tier hiding behind it.

Dionatan Simioni runs the biggest Linux YouTube channel in Brazil. He built this from Marau, a small town in Rio Grande do Sul. No VC. No team. No fundraise.

This is what Photoshop should have been from the start.

(Link in the comments)

[Javi Lopez ⛩️ @javilopen]

⚡ xAI dropped the X algorithm yesterday and I don't get why nobody noticed what's actually in there

I burned $500 on Claude going through every single line

Here's what I found (LONG POST, save it for later):

0/ Every account has an "embedding" attached to it that describes you the way AI models do: in latent space. It's the internal fingerprint the model keeps of every user, a vector of numbers that sums up how your account behaves (what topics you touch, what engagement you generate, who you interact with). The model uses it every time it decides who to show your posts to. If your history is good, it stays clean and the model pushes you. If you accumulate negative signals (blocks, mutes, reports, not_interested), it goes toxic and starts penalizing you automatically. And the trap: it does NOT reset. What you do today stays in there for weeks, poisoning everything you publish after, even if it's good.

That's why getting out of a shadowban or a low-reach streak on X feels like trying to move a giant rusted wheel. It's not your imagination, it's literally that. Cleaning up your embedding is slow and painful, like the impression you have of someone you don't like: no matter how nice they get to you, it's gonna take a while before you trust them.

Another important finding: the embedding doesn't decay on a clock. It decays with NEW engagement entering the system. If you stop posting, the old bad signals stay frozen in there. Nothing overwrites them. If you start making content the algorithm likes, you'd see improvement after 6 to 8 weeks and a real shift around 12 to 16 weeks, assuming you don't pile up more bad signals along the way.

Why is nobody talking about this? It blows my mind. Finally a confirmation of that "I'm in a bad streak" feeling we've all been through.

1/ First 30 minutes are everything

If your post doesn't get engagement fast, Grok doesn't even evaluate it. No quality score, no deep analysis, no chance of reaching anyone who doesn't follow you. Dead and buried

2/ Post age caps at 80 hours:

POST_AGE_MAX_MINUTES = 4800, bucketed in 1 hour chunks. After that you're in the "overflow bucket" which translates to "ancient, ignore"

Best window: first 0 to 12 hours. After 24 you're already in a worse bucket

Far from rewarding "evergreen" content, X wants a constant stream of fresh meat (literally the opposite of YouTube)

3/ MY BIGGEST FEAR TURNED OUT TO BE UNFOUNDED (supposedly): living in EU posting English for US audience: ZERO direct penalty in theory:

The PostCandidate struct has NO field for author country, IP, or location. Gizmoduck (X's identity service) returns only follower count + screen name. The Phoenix transformer just sees a hash of your author_id

What hurts you indirectly: timezone (your post ages while US sleeps) and the language of the POST itself

So using a VPN to "post from the US" does literally nothing (unlike TikTok or Instagram, by the way)

4/ The 5 negative signals that kill your reach:

The model predicts 22 actions per post. 5 of them are negative weights that get SUBTRACTED from your score:

- not_interested
- block_author
- mute_author
- report
- not_dwelled (people scrolling past your post without stopping)

That last one is brutal tbh. A post that gets ignored is mathematically WORSE than a post that never got published

5/ Shadowbans 100% exist. 4 different kinds:

- Hard drop. X removes your post from everyone's feed without telling you. Applied to posts with serious content (child safety, etc.) or suspended accounts. You don't even find out
- DO_NOT_AMPLIFY label. Literally a field in the code that says "do not amplify this post". If they put it on you, ads stop showing next to your posts → X stops making money from showing you → the system stops pushing you. Full blackout
- BotMaker rules. The internal panel where X employees can manually limit a specific account by hand. The code shows the categories that exist (Content, ContentLimited, Safety, Grok) but does NOT show who they're applied to or why. The tool is documented, the usage isn't
- Poisoned embedding. The worst one, as we saw above. The model has an internal "memory" for every account. If your account racks up enough "not interested" + blocks + mutes + reports over time, that memory goes toxic. From then on, even your good future posts get penalized automatically. Nobody decided this. The model just learned your account gets bad engagement and self-corrected

6/ Only ORIGINAL posts get the "Banger Screen"

Replies and retweets never enter the Grok quality classifier. If you spend your day replying to viral accounts, you're optimizing for the Reply Ranker, NOT for amplification

Want to be discovered out of network? Write originals. There's no other way

7/ Replies to small accounts get spam-scanned. Replies to big accounts get Grok-ranked

Two separate classifiers. The SpamEapiLowFollowerClassifier hits replies to small accounts. The ReplyRanker scores replies to big accounts 0 to 3 with Grok

"First!" or emoji-only replies get a 0. "Sir, this is a Wendy's" energy gets penalized. Basically, if you write replies, they better add something. Otherwise don't bother

8/ 50% of all feed requests are "shadow traffic"

is_sampled(request_id, 0.5) marks half of every feed request as shadow. Many context features (gender inference, demographics, Grok topic preferences) only activate on shadow OR with a feature flag

Translation: you literally cannot know which version of the algorithm any given user is getting. Half your audience is in an experiment at any moment

9/ Dwell (the time a user spends looking at your post before scrolling) is 5x better than getting likes

The scorer has 5 different dwell signals (dwell, cont_dwell_time, click_dwell_time, etc.) but only 1 favorite signal.

- A post with tons of likes but people read it for 1 second and keep scrolling → low score
- A post with few likes but people stay 8 seconds reading it → high score

Optimize for time spent on your post, not for likes!

10/ Things that actually work:

- Get engagement in the first 10 min. DM your friends, ping your community, whatever
- Post in your AUDIENCE'S timezone, not yours. US targeting: 8 to 11am ET (14 to 17 Madrid time)
- Don't post 5 things in a row. AuthorDiversityScorer multiplies each next post by decay^position. By post 4 you're at the floor
- Video ≥ 10 seconds. Below MinVideoDurationMs you lose the full VQV weight
- Videos with audio. Grok runs ASR (speech to text) on every video. No audio = blank signal
- Quote tweet virals in your niche. The model already knows the original engages, your value-add stacks on top

11/ Things that absolutely kill your reach:

- WILD FINDING: threads of 10+ tweets. DedupConversationFilter keeps only 1 tweet per conversation per feed. Megathreads are mathematically a waste
- Reposting the same content. Bloom filters dedupe it
- AI slop. There's literally a slop_score field in the BangerScreen output. They explicitly detect it
- NSFW/violence/hate without tags. Auto MediumRisk = no ads = structural shadowban
- Reply-spamming small accounts. Specific classifier for that

12/ What they DIDN'T release, the sneaky bastards:

The skeleton is public. The dials are not

- Exact numeric values of every weight (FavoriteWeight, ReplyWeight, OonWeightFactor, AuthorDiversityDecay). Live in xai_feature_switches::Params, external config
- The actual Grok prompts (the 7 PToS policy prompts, BangerMiniVlmScreenScore, SafetyPtos). Could literally have any framing in them
- The BotMaker rules that apply DO_NOT_AMPLIFY to specific accounts
- util/phoenix_request.rs, which constructs the final model call
- 25+ xai_* crates referenced but not included
- The production Phoenix weights. They only released the mini version

My theory: they gave us a pretty skinny skeleton of the whole thing they actually have. The muscle (weights) and the brain (prompts and BotMaker rules) are completely opaque. They kept the best parts for themselves, clearly

13/ Cheat sheet so you don't forget:

- First 30 min matter more than anything
- Your location is irrelevant, your timing and language are not
- Shadowbans exist in 4 flavors. Worst is the model quietly poisoning your author embedding from past bad signals. Climbing back up by cleaning your embedding is gonna hurt, but it can be done
- Replies and retweets don't get the quality classifier. Originals do
- Dwell (someone actually staying to look at your post) beats likes 5 to 1
- Half of all traffic is in some experiment at any moment
- They kept the best parts of the algorithm for themselves, but hey, something is something

[Zunaira Ai @ZunairaAi]

BREAKING: Claude can now research like a Stanford PhD student.

Here are 6 insane Claude prompts that turn 40+ research papers into structured literature reviews, knowledge maps, and research gaps in minutes (Save this)

[Min Choi @minchoi]

Holy smokes... humanoid robot stopped moving like a robot.

Boston Dynamics' Atlas is now moving like a gymnast.

We are cooked 🤯

[ViralRush ⚡ @ViralRushX]

SoftFoot Pro is a motorless prosthetic foot developed by Istituto Italiano di Tecnologia to mimic natural human walking with greater stability and efficiency.