Time for a thread on owning and daily-driving a @Nissan Skyline 370GT Type S. @SpaceYaMagari tagged for the culture, Patron 120 checking in.

Ain’t never lied 🤏🏽😂 treat them like they’re nobody , they wanna know who YOU are and quick

You will always find Kenyans doing the most random things in the most random places. What do you mean the Head of recruiting at OpenAI is called Liz Wamai and went to Moi University

Port injectors fitted on the A5✅️

Unpopular opinion: Mombasa is one of the ugliest and most boring cities in the world. Were it not for the ocean, it would be useless.

Wtf

Mjengo📍

My interpretation of this: Right now, Anthropic and OpenAI are making a killing by selling enterprise FDE services to F500s, building workflows for them on top of proprietary models, then using the traces and context from this to build RL envs to improve the models. This is crazy amounts of leverage - instead of buying this data they're getting paid gigantic consulting fees to extract it. This also goes way beyond typical consulting in scope - organizations are effectively outsourcing key learning curves and domain knowledge to the AI labs. Despite that, it's so far been worth it for them because the value of skilled FDE is so high and the ROI so fast, and orgs are willing to pay a premium for competent AI implementation. But in the long run, one of two things happens: either orgs are gonna get hooked on this and end up paying for the model training that replaces their business, or they find a way to build and own their own model ecosystem. What that looks like is developing some combination of AI models, evals, RL envs, and workflows. Initially probably the model will still be an off-the-shelf frontier model from a top lab. But as firms build out more sophisticated eval / RL env (increasingly the same thing) infra, it starts to become viable to post-train an custom model on top of an OSS base. Cursor have done this successfully with their Composer model RL'd on top of Kimi. Sidenote, this is the same conversation that a lot of national governments in Europe are having in the past week. When we look at what the rhetoric about 'sovereign AI' in the UK actually boils down to, it's doing custom post-training on top of an OSS model, and then running it on local GPUs. Ultimately, the current feeding frenzy for AI services in all of its guises - FDE, AI consulting, etc - should raise questions about long-term sustainability. If consulting services are truly a value add and competitive advantage, then in the long term you want to in-house.

Update: the AUR compromise appears to be ongoing After the initial incident affecting 1,500 packages, another wave of malicious AUR packages has been discovered. This time the attackers reportedly used code obfuscation to better conceal the malicious behavior. Affected packages included Node.js packages, Firefox-related packages, LibreWolf extensions, NeoVim plugins and others. If you’re using #Arch Linux and install software from AUR, I’d review recently updated packages and keep an eye on this story. phoronix.com/news/Arch-Linux…

Satya’s take on the "cognitive loop" is a must-read for the new economy. But instead of just reading about it, we put it to the test. We ran his piece through Simi, and it one-shotted the entire thesis into a perfect explainer video instantly. This is exactly what compounding human and token capital looks like in practice. The fastest way to turn dense strategy into scalable media.

💯, taxi cab Theory

AI that hacks for you. CVE-Genie automatically turns any known security vulnerability into a working exploit, end-to-end, no expertise required. Security engineering, management and governance has long relied on the gap between "a CVE exists" and "someone capable enough to weaponize it." That gap is now being closed via few API calls. github.com/BUseclab/cve-geni…

⚡️Satya is describing the new balance sheet of the firm. The old firm owned people, processes, software, customer relationships, brand, data, and IP. The new firm will own a compounding cognition loop. Every workflow becomes a training surface. Every decision becomes a trace. Every expert judgment becomes reusable signal. Every internal correction becomes model improvement. Every model run becomes a chance to turn human judgment into institutional intelligence. That is what “token capital” really means. It is accumulated machine-operable cognition. A company’s expertise becomes executable, queryable, evaluable, improvable, and portable across models. That is a massive shift. The most important line is the one about switching out the generalist model without losing the company veteran expertise. That is the entire enterprise AI war. Model providers want the firm’s knowledge to flow into the model layer. Enterprises need that knowledge to stay inside their own loop. Whoever owns the loop owns the future economic rent. Satya is laying out Microsoft’s answer to the frontier-model monopoly problem. If all company knowledge flows upward into a few foundation models, the foundation model labs become landlords of the entire economy. They absorb everyone’s expertise, commoditize every workflow, and capture the value created by every firm’s learning process. That equilibrium will trigger political backlash, customer resistance, regulatory pressure, and corporate revolt. So Microsoft’s doctrine is: every company should build its own AI learning system on top of frontier models, while Microsoft owns the infrastructure where that happens. That is elegant and self-serving. Microsoft does not need to own the single best frontier model forever. It needs to own the enterprise control plane: identity, security, permissions, data, workflow, evals, agents, memory, developer tools, cloud, compliance, and model routing. If the model becomes swappable, the platform underneath the firm’s learning loop becomes the durable asset. Satya is quietly saying the frontier model alone is unstable. A world of a few models eating every company’s expertise breaks the political economy. A world where every company builds firm-specific AI capital on top of models is more stable, more defensible, and much better for Microsoft. The “human capital gets more valuable” line is partly true and partly corporate diplomacy. High-agency humans become more valuable. People with taste, judgment, relationships, domain intuition, ambition, and the ability to direct agentic systems become much more valuable. Routine cognitive labor loses bargaining power. The future firm does not need every human equally. It needs humans who can generate high-quality signal for the loop. The human becomes a trainer, judge, strategist, relationship node, taste layer, and goal-setter. The work that cannot feed the loop or direct the loop gets compressed. This also connects directly to the Anthropic crisis. If frontier model access can be restricted, pulled, nationality-gated, or subordinated to state power, then enterprises cannot allow their intelligence layer to live entirely inside one external model. They need portability. They need private evals. They need internal memory. They need their own traces. They need model-agnostic learning systems. The model can change. The firm’s cognition loop has to survive. That is the new sovereignty test. A company that only buys AI access is a renter. A company that turns its workflows, judgments, corrections, and outcomes into a private learning loop is building capital. The deeper implication: the future economy splits between firms that compound cognition and firms that leak cognition. Firms that compound cognition will get stronger every time they operate.

Mombasa is hosting a global oceans conference! Yaaayy!! What an achievement for a city that has no sewerage system and spews its shit straight into the ocean.

Oops didn’t mean to say that out loud I thought it’s common knowledge Kenya Sewer design manual 2008

When she edges you while giving you head >>>

When men decide: are ready to marry , they don’t choose a particular woman. They just know they need to marry and have some criteria they need met . Whoever is in the picture at that time gets married

No one is bigger than the program.

The Nvidia chip inside Tesla's autopilot has a hidden bootloader disabled by fuses before the car ships. Researchers glitched the power rail at exactly the right moment to re-enable it. Full code execution >> Highest privileges >> All firmware decrypted >> Decryption keys extracted. The car that's supposed to drive itself(FSD) had a manufacturer backdoor baked into the silicon. Disabled by a fuse and defeated by a voltage spike.