Hash Bam's

Cancel the social contract.

[Hash Bam's @KryptoBams]

@treasures_io @OndoFinance @virtuals_io Incroyable pour les africains subsahariens

[Hash Bam's @KryptoBams]

@BetterCallMedhi @jokieliu Sapir whorf 👌🏻

[Md Ismail Šojal 🕷️ @0x0SojalSec]

Insane, Yeah he really did it, he made a RAM at home in his backyard shed.

While big tech cries about RAM shortages Man builds functional DRAM from scratch using homemade sputtering and lithography tools.

20-bit memory cell array, 12pF capacitance.

Turned it into a legit Class 100 cleanroom and fabricated memory cells himself. 5x4 memory cell array fabricated,This is the first RAM ever made at home.

Drug lab vibes, semiconductor god mode.

[Hash Bam's @KryptoBams]

@CryptoGirlNova Je pense vraiment qu'il n'y pas d'autres façons d'apprendre cette compétence que d'y être confronté et de s'y être cassé la gueule. C'est le seul moyen, même en ayant conscience du maximum de biais, c'est inévitable

[Spencer Baggins @bigaiguy]

A French engineer who lives quietly in Paris has spent 30 years writing software that the entire internet now runs on without knowing his name.

He wrote the code that streams every YouTube video, every Netflix show, every TikTok clip. He wrote the code that runs the virtual servers underneath AWS, Google Cloud, and Microsoft Azure. He calculated more digits of pi than anyone in history. He has no Twitter. He has no marketing. He just keeps shipping.

His name is Fabrice Bellard.

Here is the story, because almost nobody outside the systems programming world knows what one man has built.

Fabrice was born in 1972 in Grenoble, France. He studied at École Polytechnique, the top French engineering school. He never went to Silicon Valley. He never built a startup empire. He just wrote code.

In 2000 he started a project called FFmpeg, an open-source multimedia framework for encoding, decoding, and streaming video. He was 28. The project did one thing nobody else had done well. It handled every video and audio format that existed, in one library, on every operating system. He led it himself for years.

Today FFmpeg is the invisible engine of the internet. YouTube uses it. Netflix uses it. VLC uses it. Chrome and Firefox use parts of it. Every Android phone, every iPhone, every smart TV, every video editing tool you have ever touched runs FFmpeg somewhere underneath. If you have watched a video on a screen in the last 20 years, Fabrice's code processed it.

He was not done.

In 2003 he started QEMU, a machine emulator and virtualizer. He wrote it solo until version 0.7.1 in 2005. QEMU lets you run any operating system on any other operating system. It became the foundation of modern virtualization. KVM, the Linux kernel hypervisor, runs on top of QEMU. Every major cloud provider, AWS, Google Cloud, Microsoft Azure, IBM Cloud, runs virtual machines on infrastructure built around it. The Quick Emulator is the most cited piece of cloud infrastructure code on Earth.

He kept going.

In 2001 he won the International Obfuscated C Code Contest with a small C compiler that grew into TCC, the Tiny C Compiler. TCC can compile and boot a Linux kernel from source in under 15 seconds. In 2004 he calculated the most digits of pi ever computed at the time, using a personal desktop computer and an algorithm he derived himself called Bellard's formula. In 2011 he wrote a complete PC emulator in pure JavaScript that runs Linux in your browser, a project called JSLinux that engineers still cannot believe is real.

In 2019 he released QuickJS, a small but complete JavaScript engine that fits where V8 cannot. In 2021 he released NNCP, a neural network based lossless data compressor that immediately took the lead on the Large Text Compression Benchmark.

Then he turned his attention to large language models. He built TextSynth Server, a web server with a REST API for running LLMs locally. He released ts_zip and ts_sms, compression utilities that use language models to compress text and short messages at ratios traditional algorithms cannot reach. He released TSAC, a very low bitrate audio compression system. In December 2025 he released Micro QuickJS, a new JavaScript engine for microcontrollers, separate from QuickJS, designed for environments with almost no memory.

Fabrice co-founded a telecom company called Amarisoft in 2012, where he serves as CTO. Amarisoft builds 4G and 5G base station software used by carriers and labs around the world. He has been running it for over a decade while continuing to ship personal projects from his own home page at bellard dot org

He has no Twitter. He has no Instagram. He gives almost no interviews. His personal website is a flat list of projects with no styling, no fonts, no marketing copy. Just titles and links.

A quiet French engineer who never moved to Silicon Valley wrote the code that quietly runs the internet.

He is still shipping.

[Hash Bam's @KryptoBams]

@astro_samira_b Du Cameroun et en ayant fait un effort intellectuel énorme pour comprendre le contexte, je trouve juste ça lunaire

[Hash Bam's @KryptoBams]

@teh_lelouch On Baseline ?

[Hash Bam's @KryptoBams]

@Jo150800 La sorcellerie n'existe pas, c'est juste un fait social et culturel. C'est une fausse croyance et limitante. Si quelqu'un peut me prouver le contraire, Je suis preneur

[Hash Bam's @KryptoBams]

@RTLFrance @JMJancovici @ThomasSotto @moniaceleste faut vraiment qu'on en parle non ?

[Hash Bam's @KryptoBams]

@elonmusk Save Cameroon please

[Valery Melou @valerymelou]

Cette année, j'ai commencé à investir à la BRVM (Bourse Régionale des Valeurs Mobilières).

Donc j'ai cherché comment analyser les entreprises cotées. Est-ce que cette entreprise est rentable ? Est-ce que sa marge progresse ? Est-ce que son P/E est raisonnable par rapport au secteur ?

J'ai vite compris que les données existent : la BRVM publie les rapports de chaque entreprise. Mais pour avoir une vraie vue, il faut télécharger les PDF un par un, fouiller les tableaux, recalculer les ratios soi-même, et tout ça pour une seule entreprise. Recommencer pour la suivante.

J'ai essayé de trouver un outil qui fait ça proprement. Rien. Pas pour la BRVM.

Alors avec mon grand frère, expert des marchés et investisseur à la BRVM depuis bien plus longtemps que moi, on a commencé à construire un outil pour ça.

On est partis de son fichier Excel dans lequel il suivait ses investissements, vers un outil que tout le monde peut utiliser.

Ça s'appelle Vision Boursière. Tu rentres le nom d'une des 47 entreprises cotées, tu vois l'évolution de ses KPIs sur 10 ans, ses ratios, ses marges, son historique de dividendes. Tu peux comparer deux entreprises côte à côte, suivre ton portefeuille, mettre des alertes de prix.

On vient de lancer. On cherche des gens qui investissent à la BRVM ou qui veulent se lancer pour tester et nous dire ce qu'ils en pensent.

7 jours gratuits, pas de paiement requis 👇

https://t.co/8lZzkqSbaX

Si c'est ton cas, commente ou envoie-moi un message directement.

[Mustafa @oprydai]

stick to theoretical computer science and algorithm design. go in depth on computer architecture, chip design, low level C & assembly, intuitively understand the world of 1s and 0s, know the different sections of memory, you hsould be able to visualize the program in memory; go in depth on GPU architecture, marinate your brain in it, you must be able to swim through a gpu in your imagination. strip it naked to the first principles.

[nic carter @nic_carter]

The “it’s not AGI because machine intelligence is jagged” is dumb cope.

It’s obviously AGI. If you had a friend who had a 130 IQ, could write production code flawlessly, could write academic papers of a high research caliber, pass any exam in any field with flying colors, create a sophisticate LBO model, draw technical diagrams perfectly, compose poetry in any language, and could find solutions to significant unsolved mathematical problems, you would call that person a world historical genius. Certainly, no single human has ever had intelligence that “general” before.

Now you think it’s “not AGI” because it sometimes slips up and makes mistakes - so does any human that you would consider “extraordinarily intelligent.”

The professor might forget a colleagues name that he has known for a decade. He is still considered intelligent. The math genius might be a little autistic and shy, unable to maintain polite conversation. Still intelligent. You might stare at the fridge for 30 seconds unable to find the butter, despite 5 million years of evolution perfecting your visual intelligence.

We give intelligent humans a pass when they have jagged intelligence. So why the double standard?

The qualities people list as “necessary for AGI” are important traits to have, but no longer pertain to intelligence. People will say things like “true AGI requires agency, long term goal setting, embodiment, self-direct action”.

But none of those things are intelligence. Those are “things that humans have that AI lacks”. Raw intelligence, AI has it in spades. That other stuff - important yet, but broader than and different from intelligence.

The unwillingness of people to acknowledge that AGI obviously exists and has existed for a while is due to a kind of anthropic chauvinism - a psychological need to believe that humans are superior in every respect, that we possess soft skills that no machine could replicate.

Yes humans are different from machines, but if we are limiting the discussion solely to general intelligence, AI has it already. That battle is over.

If you want to reframe the discussion to matters of human dignity and personhood, fine, but that’s not an AGI question. That’s something else. Just take the loss on AGI already. It’s over.

[Starlink @Starlink]

The Starlink team is exploring ways to extend connectivity beyond our planet

[Mustafa @oprydai]

how to become a modern polymath

not by randomly learning everything.
that’s just intellectual hoarding.

a modern polymath needs structure.

what actually matters:
• build a strong spine → math, physics, computer science, writing. these fields compound into everything else
• go deep in one domain → you need one hard skill where you can actually produce real work
• go wide around it → biology, economics, history, design, psychology, philosophy. breadth gives you pattern recognition
• connect fields aggressively → innovation usually happens between domains, not inside clean academic boxes
• build artifacts → apps, essays, robots, diagrams, simulations, systems. knowledge must leave your head
• teach what you learn → if you can’t explain it simply, you don’t own it yet
• study reality, not just books → markets, machines, people, nature, institutions. the world is the real textbook

the goal is not to look smart.
the goal is to become useful across problems.

a polymath is not someone who knows random facts.

it’s someone who can move between domains,
extract principles,
connect patterns,
and build something real from the synthesis.

[Hash Bam's @KryptoBams]

@Mehdi_Scalify ça en dit long sur ton âge

[M$LITO @Djam080]

If you are from Cameroon 🇨🇲🇨🇲🇨🇲🇨🇲🇨🇲 and into Web3, crypto/Blockchain AI.
Oyaa let's connect.
Web3 🇨🇲🇨🇲🇨🇲 is growing.

[Hash Bam's @KryptoBams]

@Djam080 @jeanfrancis Nous sommes beaucoup trop timides. Je vais écrire à tous ceux ayant répondu à ce post. On verra bien avec qui ça match

[Hash Bam's @KryptoBams]

Could a VibeCoder bring the old @CryptoBubbles back to life? The updates are terrible

[Daniel Jeffries @Dan_Jeffries1]

AI will create more jobs than any other technology in history.

The doomers' fundamental error isn't just the lump of labor fallacy. It's deeper than that.

They assume a finite problem space.

This is the fundamental error of AI and job doomers. They look at the economy and see a fixed amount of work to be done, a pie that can only be sliced thinner as machines take bigger bites. They see humans a competitive resource for a finite amount of work and a finite amount of problems to solve that must be eliminated.

This is fundamentally, totally and completely wrong.

The pie isn't fixed. It never was. And the reason it isn't fixed is baked into the very nature of technology itself.

Technology is nothing but abstraction stacking. And abstraction stacking is infinite. Therefore the work is infinite.

The hammer didn't reduce the amount of work. It moved the work up the stack. And the new work was more complex, more varied, and more interesting than the old work.

Complexity breeds more complexity and more variety.

Once you have houses instead of mud huts, you have a cascade of new problems that didn't exist before. Plumbing. Wiring. Insulation. Roofing materials that don't rot. Drainage systems so the foundation doesn't flood. Fire codes so your neighbor's bad wiring doesn't burn down the whole block.

Each of those problems becomes a job. A plumber. An electrician. An insulator. A roofer. A civil engineer. A building inspector. None of those jobs existed when we lived in mud huts.

They exist because we solved the mud hut problem.

Think of all of human technological development as a stack of abstraction layers, each one built on top of the ones below it.

At the bottom: raw survival. Finding food. Building shelter. Making fire. These are the base-layer problems.

Each major technology wave solved a base-layer problem and in doing so created an entirely new layer of problems above it:

Agriculture solved "how do we reliably eat?" — and created problems of land ownership, irrigation, crop rotation, storage, trade, taxation, and governance.

Writing solved "how do we remember things across generations?" — and created problems of literacy, education, record-keeping, law, bureaucracy, and literature.

The printing press solved "how do we spread knowledge at scale?" — and created problems of intellectual property, censorship, journalism, publishing, public opinion, and democratic discourse.

The steam engine solved "how do we generate mechanical power without muscles?" — and created problems of factory design, worker safety, urban planning, railroad engineering, coal mining, labor relations, and environmental pollution.

Electricity solved "how do we deliver energy anywhere?" — and created problems of grid design, power generation, appliance manufacturing, electrical safety codes, utility regulation, and an entire consumer electronics industry.

The Internet solved "how do we connect all human knowledge?" — and created problems of cybersecurity, digital privacy, online commerce, content moderation, network infrastructure, cloud computing, social media dynamics, and an entire digital economy that employs tens of millions.

Notice the pattern?

Each solution didn't just solve a problem.

It created an entirely new problem space that was larger, more complex, and more varied than the one it replaced.

The stack grows. It never shrinks.

It's turtles all the way down and all the way up.