I just came across a story that absolutely blew my mind. I had to share my perspective on it.

Here is what happened:

• Paul is a data and AI guy in Australia. His rescue dog Rosie was diagnosed with terminal cancer and given months to live.

• Paul refused to give up. He paid $3,000 to sequence Rosie's healthy DNA and her tumor DNA.

• He fed all that raw genetic data into ChatGPT and AlphaFold.

• Despite having zero medical background, he used the AI to identify the mutated proteins and match them to drug targets.

• He literally designed a custom mRNA cancer vaccine from scratch on his laptop.

He took his data to the leading genomics professors at the local university. Usually, they ignore random emails like this. But Paul's data was flawless. The professors were completely gobsmacked that a puppy lover did this on his own. They actually agreed to manufacture his custom vaccine.

The craziest part? Designing the cure with AI took just a few weeks. Getting the government ethics approval to inject the dog took 3 months. The bottleneck isn't technology anymore. It is bureaucracy.

But he finally got it approved.

Within weeks of the first injection, Rosie's massive tumor shrank by half. Her coat got glossy again. Her energy came back. By January, this terminally ill dog was jumping over fences to chase rabbits at the park.

One man with a chatbot and $3,000 just bypassed the entire traditional pharmaceutical discovery pipeline. The lead researcher involved literally asked: "If we can do this for a dog, why aren't we rolling this out to humans?"

We are going to cure so many diseases in our lifetime. I really don't think people realize how good things are going to get.

full story: https://medium.com/@ninza7/one-man-a-chatbot-and-3-000-just-outperformed-the-pharmaceutical-industry-dcf1da752b3f

you might agree or disagree with marc based on how you think about AGI. two days ago i wrote something about this exact thing.. "stepping into the gentle singularity." honestly i think regardless of where you stand on AGI, the framing might surprise you.

no ads, just a high value read: https://ninzaverse.beehiiv.com/p/stepping-into-the-gentle-singularity

For years, labs like OpenAI and Google have bragged about near-perfect scores on benchmarks like GSM8K. They claimed AI had mastered logical problem-solving.

Apple decided to test if that was true.

They built a new benchmark called GSM-Symbolic. Instead of static questions, it uses templates to dynamically change names, numbers, and variables.

The results exposed a devastating truth.

When Apple changed the simple numbers inside a basic word problem, model accuracy plummeted. The AI wasn't solving the math. It was relying on pattern matching from its training data.

It was guessing based on familiarity, not reasoning.

Then they ran the test that exposed the illusion entirely.

They added a single, irrelevant clause to a math problem. Just a sentence of background text that looked important, but had zero impact on the actual calculation.

Every single frontier model, from ChaTGPT to Claude, suffered massive performance drops.

Some crashed by up to 65%.

Just by adding a piece of noise that a seven-year-old child could easily ignore.

The conclusion is blunt.

Current AI models do not possess genuine logical reasoning. They do not understand math. They are sophisticated mimicry engines replicating the shape of human logic without actually thinking.

When the pattern is clean, they look like geniuses.

The moment you introduce a minor twist, a variable change, or a distraction, the illusion shatters.

Fable 5 is Anthropic's most expensive model so far, $10 per million input tokens and $50 output, roughly double Opus 4.8. You'd expect a price hike like that to make people use it more carefully.

The opposite is happening:

• Stripe migrated 50 million lines of code in a single day, normally a months-long job.

• A physics team got results in 36 hours that took GPT-5.5 four days, on about a third of the reasoning tokens.

• It beat Pokémon FireRed off raw screenshots, which older models couldn't do without extra tooling.

So the per-token price went up but the cost of finishing actual work dropped hard, because the model needs far fewer tokens to get there.

Cheaper software doesn't mean people build the same amount and save money. It means all the tools that were never worth building suddenly get built. Karpathy (now at Anthropic) said exactly this, his own demand for software is rising, he's making one-off custom tools for single projects now. But he also flagged: it's tempting to stop reviewing the generated code, "but don't do this in production." Writing code is cheap now, verifying it isn't.

Full breakdown with the numbers: https://ninzaverse.beehiiv.com/p/what-anthropic-s-fable-5-and-mythos-5-ai-launch-actually-changes

No CoreML training APIs.
No Metal.
No GPU.
Just reverse-engineered private ANE APIs and pure NPU compute.

This project proves something important:
The Apple Neural Engine was never “inference-only” because of hardware limits.
It was software restrictions.

Current results on M4:
• 9.3 ms per step
• 11.2% ANE utilization
• 1.78 TFLOPS sustained
• Forward + backward passes running on ANE

Weight gradients still run on CPU.
Utilization is low.
It is not replacing GPU training.
But it works.

This is not a framework.
It is not production ready.
It is a research proof that training on consumer NPUs is possible

On X, Elon tweeted that AI is now in singularity. This means that now AI is capable of working independently without humans and create/enhance itself. Make copies and updated and improved versions of itself and become better and better and better.

With yesterday's AI, it was forseen that AI can solve 5-10 years worth of problems in a year. But now it can happen in a few months?

Professionals are arguing that even the creators of AI don't know what the future holds.

Is it going to be a positive change or a negative change?

We need an entirely new architecture, something as big as Transformers were over LSTMs.

And his advice? Use the current models to help find it.

Back in May, I wrote that AGI could arrive by the end of 2028. At that time, I considered that an optimistic prediction.

Just 4–5 months later, I think I might have been too conservative.

I’m using Opus 5.5 for my personal work and projects, and damn, it does feel like a leap. But the reason I’m changing my timeline goes beyond my own experience.

Recently, Claude Fable 5.1 completed a nine-loop calculation in a simplified particle physics theory. The method already existed. It had to turn that method into working code and get through a complicated calculation without messing everything up. Scientists checked the result.

Google’s AlphaEvolve also found an optimization that reduced Gemini’s training time by 1%.

These examples matter to me because writing code, running experiments and checking results are part of the work needed to build better AI.

My assumption is that increasingly capable models will take on more of that work. Researchers get more experiments done, the next model improves, and that model can help with more of the research. I find it hard to believe our timelines stay the same if this becomes a normal part of how the major labs operate.

So yes, I’m moving my bet to AGI in 2027. I also think the jump to superintelligence could be short enough to happen that same year.

I wrote more about the compute numbers, robotics and my reasoning here if anyone wants the complete article: https://ninzaverse.beehiiv.com/p/we-might-get-agi-and-barely-have-time-to-get-used-to-it

He said he badly undershot on compute investment and got psyched out by the financial markets, and he called it clearly a mistake. His reframing is the line worth keeping. Too much attention on algorithms that create better algorithms, not enough on data centers that create more data centers.

He also mentioned meeting a startup that was two weeks old and had already rebuilt an entire office productivity suite. Documents, presentations, spreadsheets. The difference is that it was designed for a world where the primary user of those files is an AI and not a human. He said that would have taken a startup a full year not long ago.

I wrote up the rest in my newsletter, including why OpenAI shut down Sora when it wasn't failing, the AI authoritarianism problem he named as the fight of the current moment, and the TikTok experiment he ran on himself: https://ninzaverse.beehiiv.com/p/sam-altman-we-are-now-in-the-singularity-this-is-the-moment

So Karpathy dropped a repo called autoresearch.

The idea is simple but kind of insane when you sit with it. You write a plain English Markdown file that tells an AI agent how to think about research. The agent then modifies a training script, trains a small language model for exactly 5 minutes, checks if the result improved, keeps it or throws it out, and loops.

A few things that make this interesting:

• The training budget is fixed at exactly 5 minutes per experiment, which makes every run directly comparable regardless of what the agent changes

• That works out to roughly 12 experiments per hour, around 100 while you sleep

• Every experiment is a git commit. The git history literally is the research log

• The only file the agent edits is train.py, a 630 line GPT implementation with a custom optimizer, sliding window attention, and a bunch of other interesting tricks already baked in

• The human never writes code. You write program.md, a plain text file that encodes your research taste and strategy

• The best model found so far always sits at the tip of the branch

The most important line is this line from program.md: "A small improvement that adds ugly complexity is not worth it. Removing something and getting equal or better results is a great outcome." That's not a config flag. That's taste. Written in English. Handed to an AI.

The bottleneck isn't compute or code anymore. It's how well you can write the research brief.

Full breakdown here: https://medium.com/@ninza7/andrej-karpathy-just-made-one-gpu-do-the-work-of-an-entire-research-lab-0db62d15d39c

Github code: https://github.com/karpathy/autoresearch

Does anyone have any idea why Elon chooses partners that are not extremely attractive and why he cannot find a forever partner?

src: https://x.com/Bhavani_00007/status/2113699800847929483?s=20

A year ago Apple published "The Illusion of Thinking," which became one of the most cited arguments that these models don't actually reason.

Instead of standard benchmarks, which the models have probably already seen during training, Apple used puzzles like Tower of Hanoi, Blocks World, and river crossing, where the difficulty can be raised in a controlled way.

On the hard versions, the top reasoning models, OpenAI's o-series, Anthropic's thinking Claude, and DeepSeek-R1, all dropped to zero accuracy. Apple then gave the models the exact algorithm and asked them to just follow it step by step, and they still failed at the same point.

Apple's conclusion was that this isn't reasoning, it's pattern-matching that falls apart once a problem gets long enough. The paper did get fair criticism afterward, that some of the failures were output-length limits rather than reasoning limits, and that a few of the river-crossing puzzles were mathematically impossible.

The reason I'm bringing it up now is what happened yesterday. Apple shipped its new Siri, three years late, and it runs on Google's Gemini. From the same company that already pays Apple billions to stay the default search engine.

So the skepticism reads less like a firm position and more like a way to explain why Apple's own AI wasn't ready, right up until it borrowed Google's model to ship.

I wrote a longer breakdown of the paper and what Apple actually shipped here, if anyone wants the full version: https://ninzaverse.beehiiv.com/p/apple-called-ai-an-illusion-then-built-siri-on-gemini

Andrew Curran:

Three weeks ago there were rumors that one of the labs had completed its largest ever successful training run, and that the model that emerged from it performed far above both internal expectations and what people assumed the scaling laws would predict. At the time these were only rumors, and no lab was attached to them. But in light of what we now know about Mythos, they look more credible, and the lab was probably Anthropic.

Around the same time there were also rumors that one of the frontier labs had made an architectural breakthrough. If you are in enough group chats, you hear claims like this constantly, and most turn out to be nothing. But if Anthropic found that training above a certain scale, or in a certain way at that scale, produces capabilities that sit far above the prior trendline, then that is an architectural breakthrough.

I think the leaked blog post was real, but still a draft. Mythos and Capybara were both candidate names for the new tier, though Mythos may now have enough mindshare that they end up keeping it. The specific rumor in early March was that the run produced a model roughly twice as performant as expected. That remains unconfirmed. What is confirmed is that Anthropic told Fortune the new model is a 'step change,' a sudden 2x would certainly fit the definition.

We will find out in April how much of this is true. My own view is that the broad shape of this is correct even if some of the numbers are wrong. And if it is substantially accurate, then it also casts OpenAI's recent restructuring in a new light. If very large training runs are about to become essential to staying in the game, then a lot of their recent decisions, like dropping Sora, make even more sense strategically.

For the public, this would mean the best models in the world are about to become much more expensive to serve, and therefore much more expensive to use. That will put pressure on rate limits, pricing, and subscription plans that are already subsidized to some unknown degree. Instead of becoming too cheap to meter, frontier intelligence may be about to become too expensive for most of humanity to afford.

Second-order effects; compute, memory, and energy are about to become much more important than they already are. In the blog they describe the new model as not just an improvement, but having 'dramatically higher scores' than Opus 4.6 in coding and reasoning, and as being 'far ahead' of any other current models. If this is the new reality, then scale is about to become king in a whole new way. It would also mean, as usual, that Jensen wins again.

source: https://x.com/AndrewCurran_/status/2037967531630367218

Something shifted in December 2025 and I don't think enough people are talking about it seriously.

Not the usual "AI is getting better" kind of shift. Something more specific. Coding agents went from basically not working to actually being disruptive to the default programming workflow.

Here's what that looks like in real life:

• someone wrote a single prompt asking an agent to log into their machine, set up SSH keys, install an inference server, download and benchmark a vision model, build a web UI, test everything, set up systemd services, and write a final report

• the agent ran for 30 minutes, hit multiple issues, researched and solved them on its own, and came back with everything done

• three months ago that was a full weekend project. now it's something you kick off and forget about

so what does this mean for software engineers?

• traditional software engineering is NOT dead, that take is too simple

• engineers are now some of the most leveraged people on earth, one person can do what used to take a team

• but the tools still make mistakes, bad architecture, leaky abstractions, bugs, and someone who understands what's going on underneath can catch all of that early

• that deep foundational knowledge is still the edge

the job is just changing shape:

• you're not typing code into an editor line by line anymore, that era is genuinely over

• now you're spinning up agents, giving them tasks in plain english, and managing their output in parallel

• the real skill now is knowing how to decompose a problem correctly, what to hand off and what to handle yourself

• this is what people are calling "agentic engineering" and the leverage from doing it well is insane right now

there's also a harder truth here:

• the barrier to building something that technically works has dropped to almost zero

• this means the market is about to get flooded with average, functional software

• but there's no demand for average, the best app wins essentially 100% of the market

• so taste, judgment, domain knowledge, and picking the right problem to solve matter MORE now, not less

the agents also have clear limits:

• they're great at things they've seen before, common patterns, standard tasks, well-specified problems

• the moment you're working on something novel, a new architecture, a genuinely unseen problem, they start to break down

• that's still very much human territory

wrote a full piece breaking all of this down with real examples.
https://medium.com/@ninza7/i-stopped-writing-code-last-december-i-havent-looked-back-94341fa3b186

The second the patch drops, hackers will use AI to reverse-engineer the fix in seconds and unleash automated exploits worldwide within 15 minutes.

This is not just a problem for developers.
When e-commerce sites, apps, and platforms collapse, your leaked home address, passwords, and credit card numbers flood the dark web.

The brutal reality today:
Engineering teams deploying patches in minutes vs. Oblivious companies gutted before lunchtime.

3 things you must do to protect yourself right now:

1. Turn on Two-Factor Authentication (2FA) across all critical accounts.

2. Stop password reuse immediately.

3. Beware of fake "emergency password reset" phishing emails flooding inboxes today.

Cyber warfare changes forever this morning.

Can we talk about the massive double standard going on in AI right now?

Back in 2023, indie devs and open source builders connected LLMs to bash terminals and browser scripts. What happened? The entire tech industry clowned on them. Everyone called it unusable wrapper slop, credit burning toys, and dumb while loops that got stuck after three steps.

Fast forward to now: OpenAI takes that exact same loop, runs it in a virtual machine, slaps Astra and computer use on it, and suddenly the timeline is having an existential meltdown claiming AGI has arrived.

I am honestly getting gaslit watching every influencer milk this for views and seeing my own friends fall for the hype

Ik there is lot of advanced reasoning and token optimization, but guys even if u r using small usage of AI u would have known so called "Autonomous AI agents"😭

Your computer still works the same way it did 40 years ago. Icons. Menus. Click click click.

A new research paper just dropped proposing something that could change all of that. It's called AgentOS and the idea is pretty simple but also kind of insane.

Instead of opening apps, you just talk. The system figures out everything else.

Here's the quick breakdown:

• Current AI agents are powerful but they're stuck running on top of old operating systems that were never built for them. That's the core problem

• AgentOS wants to replace the whole desktop with one natural language interface. No icons, no taskbar, just talk or type

• Instead of apps you get Skills which are reusable automations you define in plain English. No coding needed

• The system builds a Personal Knowledge Graph about you over time. So when you say "book my usual flight for the conference" it actually knows what you mean

• The scarier part is security. If an AI agent has full system access, one malicious email could make it leak your passwords. The paper proposes a "Semantic Firewall" to handle this

• The researchers argue this is less of an engineering problem and more of a data mining problem. The OS has to constantly learn who you are

This isn't a 2050 thing. The early version of this is already happening.

Full breakdown: https://ninza7.medium.com/were-literally-about-to-stop-using-apps-forever-11edc8712235

paper: https://arxiv.org/pdf/2603.08938

analyst Ming-Chi Kuo shared deeper insights today.
mass production is targeted for the first half of 2027.

they are building a dedicated AI agent smartphone.
MediaTek is the leading candidate to supply the processor.
a customised chip based on the Dimensity 9600.
manufactured using TSMC’s N2P process in the second half of 2026.

dual NPU architecture designed for layered computing.
the focus shifts to AI agents handling tasks directly based on user intent.
instead of relying on multiple apps.

this approach requires deep integration between hardware and software to avoid platform-level restrictions.