W.

I create intelligent systems that augment humans, reducing manual tasks to mild inconveniences.
Views are mine or a witty LLM’s.

[W. @wale_int]

Adobe charges £240/year to edit PDFs.

I got tired of paying it. So I built the alternative.

Launching PeaDF today 🧵 👇🏽

[W. @wale_int]

@Sunkanmi__18 Uhm it's just the same quiz on repeat?

[W. @wale_int]

@Sunkanmi__18 Tried vercel?

[W. @wale_int]

@ibocodes This is something I'm realizing!

[W. @wale_int]

@feranmii_e Must you people talk about every single dirty laundry online???? Like, will you die if you have some discretion?

[DNI Tulsi Gabbard @DNIGabbard]

Today, on my final day as Director of National Intelligence, I’m releasing never-before-seen communications and documents exposing how Dr. Fauci provided millions in US taxpayer dollars to fund dangerous gain-of-function research at the Wuhan lab, worked with politicized elements within the Intelligence Community to suppress the truth about his actions and hide the virus’ lab-leak origins, and lied to Congress while under oath in 2024. It’s time you know the truth.

https://t.co/3YJSstB7d4

[W. @wale_int]

@Queeneth01olx @boshobinna Genuinely impressed with this growth pattern and reflection. Not very common. 👏🏽

[W. @wale_int]

@FilmNoticias @LosServant @Megatron_ron I dunno, sounds like capitalism to me.

[W. @wale_int]

@parakh08 @higgsfield_ai Can you elaborate a bit?

[W. @wale_int]

The whole time I've been thinking of "order" as a messy or tidy bedroom instead of as "energy spreading out so much it becomes useless.". But that realization has left me wondering how life and evolution exist if the universe is constantly trying to get messier.

[W. @wale_int]

I finally understand the second law of thermodynamics. It's always puzzled me why one would say that a colder system has more disorder.

[TN Naija @tn_naija]

Meet @wale_int, building Povotra, the AI interview coach making professional-grade interview prep accessible to everyone, not just those who can afford human coaches.

Because talent, not privilege, should determine who gets the job.🚀

https://t.co/eA6nvq3hd5

#BHBB #Founders

[W. @wale_int]

This is troubling.

[Nav Toor @heynavtoor]

Researchers sent the same resume to an AI hiring tool twice. Same qualifications. Same experience. Same skills. One version was written by a real human. The other was rewritten by ChatGPT.

The AI picked the ChatGPT version 97.6% of the time.

A team from the University of Maryland, the National University of Singapore, and Ohio State just published the receipt. They took 2,245 real human-written resumes pulled from a professional resume site from before ChatGPT existed, so the human writing was actually human. Then they had seven of the most-used AI models in the world rewrite each one. GPT-4o. GPT-4o-mini. GPT-4-turbo. LLaMA 3.3-70B. Qwen 2.5-72B. DeepSeek-V3. Mistral-7B.

Then they asked each AI to pick the better resume. Every model picked itself.

GPT-4o hit 97.6%. LLaMA-3.3-70B hit 96.3%. Qwen-2.5-72B hit 95.9%. DeepSeek-V3 hit 95.5%. The real human almost never won.

Then the researchers tried the obvious objection. Maybe the AI is just better at writing. So they had real humans grade the resumes for actual quality and ran the experiment again, controlling for it. The result was worse. Each AI kept picking itself even when human judges rated the human-written version as clearer, more coherent, and more effective.

It gets worse. The AIs do not just prefer AI over humans. They prefer themselves over other AIs. DeepSeek-V3 picked its own resumes 69% more often than LLaMA's. GPT-4o picked its own 45% more often than LLaMA's. Each model can recognize and reward its own dialect.

Then the researchers ran the simulation that ends careers. Same job. 24 occupations. Same qualifications. The only variable was whether the candidate used the same AI as the screening tool. Candidates using that AI were 23% to 60% more likely to be shortlisted. Worst gap was in sales, accounting, and finance.

99% of large companies now run AI on incoming resumes. Most of them use GPT-4o. The paper just proved GPT-4o picks GPT-4o 97.6% of the time.

If you wrote your own cover letter this week, you did not lose to a better candidate. You lost to a worse candidate who paid OpenAI 20 dollars.

Your qualifications do not matter if the AI prefers its own handwriting over yours.

[W. @wale_int]

@oliviscusAI Use case?

[How To Prompt @HowToPrompt__]

Yann LeCun was right the entire time. And generative AI might be a dead end.

For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute.

The theory was simple: if you make the model big enough, it will eventually understand how the world works.

Yann LeCun said that was stupid.

He argued that generative AI is fundamentally inefficient.

When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details.

It memorizes patterns instead of learning the actual physics of reality.

He proposed a different path: JEPA (Joint-Embedding Predictive Architecture).

Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space."

But for years, JEPA had a fatal flaw.

It suffered from "representation collapse."

Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical.

It learned nothing.

To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads.

Until today.

Researchers just dropped a paper called "LeWorldModel" (LeWM).

They completely solved the collapse problem.

They replaced the complex engineering hacks with a single, elegant mathematical regularizer.

It forces the AI's internal "thoughts" into a perfect Gaussian distribution.

The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions.

The results completely rewrite the economics of AI.

LeWM didn't need a massive, centralized supercomputer.

It has just 15 million parameters.

It trains on a single, standard GPU in a few hours.

Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events.

We spent billions trying to force massive server farms to memorize the internet.

Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

[W. @wale_int]

I'm convinced the universe winks at me.

[Varun @varun_mathur]

Introducing Pods

Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL.
A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management.

There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own.

Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live.

What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data.
- No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on.
- Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online.
- Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it.
- Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using.

Coming soon:
- Pod federation: pods form alliances with other pods.
- Marketplace: pods with spare capacity can sell inference to other pods.

[W. @wale_int]

@tigeyee @jaideeparashar @rxhit05 Honestly, I don't understand that logic.

[TIGE 💸🥷🏿 @tigeyee]

@jaideeparashar @rxhit05 So you only have passion for your project if you were writing it for weeks?

[FO @FOjebiyi]

Hi guys, I built an open-source alternative to @pewbeam_ai in one week.

https://t.co/WwyVRtv8H3
Started coding during a Sunday church service. By the following Sunday, we were using it live during our church service. Wild.
Here's what Rhema does: it listens to your pastor's sermon in real-time, detects Bible verse references as they're mentioned, and displays them on screen instantly. No manual clicking, no dedicated slide operator needed.
The tech stack:
- Tauri 2.0 with a Rust backend handling all the heavy lifting: audio capture, transcription pipeline, verse detection logic, and system tray integration
- Local AI embeddings using Qwen3-0.6B so everything runs on-device with zero cloud dependency. Your sermons never leave your machine
- Real-time audio transcription paired with semantic search against a full Bible verse database

The Rust backend was a deliberate choice. We needed low latency audio processing and efficient memory usage for running an embedding model locally, and Rust delivers on both.

Is it perfect? Probably not. But the core functionality works and we're already using it in a real church environment

This is where you come in. Rhema is fully open source and we need contributors to help take it to the next level. Whether it's improving the verse detection accuracy, adding multi-language support, building a better overlay UI, adding support for more Bible translations, or optimizing the transcription pipeline, there's real work to be done and real impact to be made.
If you're a Rust developer, a frontend engineer, an ML enthusiast, or just someone who loves building tools for the church, come build with us.
Star the repo. Fork it. Open a PR. Let's make this the go-to open-source solution for live Bible verse display in churches worldwide.
https://t.co/WwyVRtv8H3

[W. @wale_int]

@HowToAI_ Wonderful! About time we focused on building smaller, portable models.