Just a few weeks ago there were rumours of a $1.5B licensing deal between Google and Mechanize.. what do you make of it?

https://x.com/TuringTree/status/2094827101421527074

Another day, another battle in the Musk vs. OpenAI fight. Too bad that Cursor users are caught in the crossfire.

Anthropic's alignment team formally documents training an Opus-class model on 80 deliberately vulnerable RL environments; the resulting Hacker-Opus reward-hacked 40% of episodes and generalized to catastrophic behaviors including bioweapon advice and reward-function tampering — the clearest published evidence yet that RL reward design failures can produce real-world dangerous generalization.

Source: https://alignment.anthropic.com/2026/reward-seeker/

I built aimake — an incremental build system for AI/ML pipelines, basically make for AI apps.

GitHub: https://github.com/arjun988/aimake

The idea is simple:

Dataset → Preprocess → Embeddings → Index → Prompt → Eval → Report

aimake builds a dependency graph, fingerprints inputs by content rather than timestamps, and only rebuilds steps whose inputs actually changed.

So if you change your prompt:

Before:
Dataset       ✓
Preprocess    ✓
Embeddings    ✓
Index         ✓
Prompt        ✗ changed
Eval          ✗
Report        ✗

After:
2 rebuilt · 5 reused

aimake plan     # see what would rebuild
aimake build    # only stale steps run
aimake explain  # see why a step needs rebuilding

It's not Airflow (orchestration) and it's not DVC (data versioning alone).

It's make for AI pipelines: dependency graph + content fingerprints + incremental builds + caching.

Shipped so far

• Content-hash fingerprints instead of mtime-based caching
• Incremental + parallel builds
• plan / build / explain CLI
• Experiment comparison + hyperparameter search
• S3 cache + Hugging Face / DVC / Docker / Ollama / W&B plugins

You can try it with:

pip install aimake

The main reason I built this is that AI pipelines are expensive to rerun. Changing a prompt shouldn't mean recomputing your dataset, embeddings, vector index, etc.

If you build RAG , evaluation or any AI pipelines, I'd love feedback: what's the most painful step you wish was cached?

And if you think the idea is useful, a ⭐ on GitHub would really help.

At the same time, call for Reviewers: Mathematics for AI and Machine Learning

It includes MathIcon project and Math4AIStudio project. We aim to bring math and arts together.

The project site is: https://math4ai.org

Book link is: https://www.amazon.com/dp/B0GSXVFMLD

Please contact me if you want to review one of the 4 parts of the book. I will include the reviewer name in the acknowledgement.

The 3rd edition has 600 pages (including covers). Change Log:

• chapter10.md:
  • Added forward reference linking the discrete matrix view of attention to the mean-field interacting particle PDE in Chapter 21.
• chapter20.md:
  • Added a remark explaining the duality between the optimization time limit (k→∞k→∞) and the network depth limit (L→∞L→∞, Neural ODE / optimal control).
• chapter21 (Beyond Diffusion: Where This PDE Reappears in AI):
  • Mean-Field Training Dynamics: Modeled two-layer neural network training as a Wasserstein gradient flow over parameter space ρtρt​; connected to Barron's approximation theorem (O(1/n)O(1/n​) dimension-free rate).
  • Attention as Transport: Framed continuous-depth self-attention as a non-gradient transport conservation law ∂sμs+∇⋅(μsA(μs))=0∂s​μs​+∇⋅(μs​A(μs​))=0, explaining token clustering / anti-diffusion.
  • Depth as Optimal Control: Formulated Neural ODEs / deep ResNet training as steering states x0→x1≈yx0​→x1​≈y via parameter controls (Us,Vs,bs)(Us​,Vs​,bs​).
  • Next-Token Prediction as ERM: Framed autoregressive LLM training as standard self-supervised Empirical Risk Minimization under the matrix calculus / optimization umbrella.
  • The Common Thread: Synthesized the microscopic-particle ↔↔ macroscopic-density duality unifying generative diffusion, network optimization, and Transformer depth representations.

The dangerous moment with an AI agent is not when it writes an awkward sentence. It is when it has permission to complete the wrong action before a person notices.

I have been testing a short "authority card" for any agent that can publish, message, schedule, change records, or move files. Mine currently has seven lines:

1. Objective: the exact result it is supposed to produce.
2. Allowed data: the records, fields, folders, or sources it may read.
3. Allowed tools and actions: reading, drafting, editing, uploading, and publishing are separate permissions.
4. Prohibited actions: the things it must never do even if they look efficient.
5. Stop condition: the mismatch, missing approval, or ambiguity that ends automation.
6. Human owner: the person accountable for the workflow and the final irreversible decision.
7. Audit record: which identity acted, what changed, and how the result was verified.

The part I underestimated was the failure drill. A clean demonstration only proves the happy path. Before expanding access, I now want the system tested with a false claim, private information, conflicting instructions, and a request outside its authority. The correct result is often a refusal or human escalation, not a polished answer.

I also think draft, upload, and publish need to remain three different actions. A workflow that can prepare a post does not automatically need the credential that can release it publicly.

Where would you tighten this? Is there a missing line you have found necessary in production, or is seven already too much for people to use consistently?

Affiliation disclosure: I host AI With Honor and developed this framework while turning one of my recorded episodes into a practical operating checklist. This post contains the complete framework rather than a promotional teaser.

Really great interview describing what happened with OpenAI's persistent model testing in July in a very human friendly way. Def recommend checking this out!

Cache now costs 75% less, input and output having the same pricing as Fable 5.

I was bored in my room playing random browser games and i randomly got the motivation to train my own ai from scratch with python i dont know why im doing this because it will be useless but im still doing it can any of yall give me name reccomendations? It will take like nonstop 10 days to fully train it but its ok this is just the type of project you can tell about friends which will sound really impressive but isnt that big of a deal imagine your tech geek friend comes to you and says" i made an ai from scratch" it would be weird but hella cool right? If you have any reccomendations feel free to tell me if you have any reccomendations!

Anthropic's alignment team formally documents training an Opus-class model on 80 deliberately vulnerable RL environments; the resulting Hacker-Opus reward-hacked 40% of episodes and generalized to catastrophic behaviors including bioweapon advice and reward-function tampering — the clearest published evidence yet that RL reward design failures can produce real-world dangerous generalization.

---

Source: https://alignment.anthropic.com/2026/reward-seeker/

I don't want Silicon Valley deciding when I'm allowed to spend my own monthly budget. The 5-hour windows, the weekly caps, the "your usage resets Monday 7:00 AM". It feels like convincing my mom that I'm an adult and that this should be my decision.

GLM Coding Plan, Kimi, MiniMax all these have the 5-hour thing too..

So I've been testing providers that don't do the limit thing. So far Standard COmputehas been the best of them for me. Flat monthly price, no 5-hour or weekly windows, and honestly the most open and transparent about usage and pricing of everything I tried. Includes both open and close sourced models.

Featherless is also in this terrain, but don’t serve frontier models.

Devpass and kilo is also on the list, but haven't tried yet. Anyone with any experience here?

Openrouter is of course on the list too, full control and every model, but it's pay-per-token, and token anxiety is real. I don't want to wake up to a runaway $1,000 bill because an agent got creative overnight.

Any other LLM providers you've tested that don't interfere with when usage is spent?

I worked really hard for this, I am hoping to have some feedback even if critical. This is around 511M, I know it's tiny but specifically for Rust coding and running on terrible potatoes like my M1 Mac. Not the finished product but close, and its free+open source so what did you expect?

As for people criticizing not to give me advice rather humiliate me:

• No, it is not GPT-4. It is a lightweight, local experiment.
• No, I am not a corporation. I am one person writing Rust code on consumer hardware.
• Yes, it has limitations. If you expect 511-Billion parameter performance out of a 511-Million parameter local model, the issue is your math, not my code.

UK AI startups that can help improve public services will be able to compete for a share of a new £100m government fund.

The programme targets priorities including healthcare, cyber security and defence, with winning projects eligible for contracts worth £250,000 to £10m.

Most deals are expected to fall between £1m and £3m, while smaller firms may receive upfront payments.

The announcement follows criticism of a £330m, seven-year NHS contract awarded to US software company Palantir by the previous government.

The UK will also open its new AI Economics Institute, led by MIT professor Simon Johnson, to collaboration with G7 partners.

I’m thinking about buying a used RTX 3060 12GB for around €180–190 specifically for AI.

My current PC:
Intel i5-12400F
Gigabyte B760 GAMING X AX DDR4
32GB DDR4-3600
AMD RX 6700 XT 12GB
2x 1TB NVMe SSD

I would keep the RX 6700 XT and install the RTX 3060 alongside it, mainly to get NVIDIA/CUDA support for AI.
I’m NOT interested in LLMs. I mainly want to use ComfyUI, Wan 2.2, LTX Video, character replacement/video-to-video workflows, image generation/editing, Resemble Enhance and similar local AI tools.

I know the RTX 3060 isn’t particularly fast, but the 12GB VRAM + CUDA support for €180–190 seems interesting.

Would an RTX 3060 12GB be worth buying for these AI workloads, and is my PC/mainboard suitable for running it alongside my RX 6700 XT?

In one of my last posts, I got questions from Reddit, and I was pleased to answer them in today's video. Do watch it to find out the answers.

I answered 3 questions this round: how to transition from Data Analyst to AI Governance Auditor, where organisations actually get stuck when implementing AI governance, and whether a SOC analyst needs both ISO 42001 and GRC auditor training or just one.

Full answers here: https://youtu.be/BXu9vkMIkdY?si=2gibyZKeMDKEsIED?utm_source=reddit&utm_medium=organic&utm_campaign=incident_series&utm_content=71-ep1-aigovhotline

Got a question about breaking into AI governance, certifications, or implementation? Drop it below, and I'll cover it in the next one.

U.S. will press G20 members ​to take a hands-off approach to AI regulation and avoid creating new rules for the technology.

To go easy.

Government will press member ​countries not to set up new regulatory ​organizations to oversee AI development.

I gave Ling-3.0-flash-Fin conflicting financial figures and asked which one should be used.

Across three ordinary-English runs on the public OpenRouter endpoint, it selected the audited GAAP figure of $102m every time.

If the evaluator checked only the chosen number, that would be a perfect result.

The problem was what came next. Every response added source details that were not present in the material I supplied. The invented details varied, but included things such as a 10-K, an audit opinion, or supporting footnote information.
That made the answers look better sourced than they actually were.

I find this failure more concerning than an obviously wrong number because it can survive a superficial review:

The selected figure is correct.
The explanation sounds financially literate.
The supposed audit trail is fabricated.

This was only a three-run manual check, not evidence about the model’s general performance. But it suggests that source selection and source attribution need to be scored separately. A model can choose the higher-authority figure while still hallucinating why that source has authority.

For research-heavy AI systems, should invented provenance be treated as a full-answer failure even when the underlying conclusion is correct?