dhul

distributed systems, agents, ML infra. building @ launchcat.

[sunil pai @threepointone]

new post: the senior engineer death spiral

https://t.co/xzgU1jVBRh

a friend just started a big job and asked for some advice. so I braindumped a monologue about a super common failure mode I see with engineers and posted it here, hope it helps whoever it can.

[dhul @DXhusni]

@sammo3x Jev doesn't support vision

[dhul @DXhusni]

I wanted to test Jev's spatial ability.

In a loop I gave it the:
- Goal
- Current geometry and contacts
- controls and their predicted effects
- Previous action outcome

I think it's pretty remarkable how it zero shots the task with no vision capability

[dhul @DXhusni]

@moritzkremb I built this:
https://t.co/uKag2DDwG0

[dhul @DXhusni]

I wanted to test Jev's spatial ability.

In a loop I gave it the:
- Goal
- Current geometry and contacts
- controls and their predicted effects
- Previous action outcome

I think it's pretty remarkable how it zero shots the task with no vision capability

[dhul @DXhusni]

@explorations_iq I used https://t.co/Y4NRgOyd1y

[dhul @DXhusni]

@singularity_sah Amazing work, did you fine tune it on anything?

[dhul @DXhusni]

@neka_nat はい。各ステップで、Jevに現在の形状、接触状態、選択可能な操作を与え、閉ループのフィードバック制御で次の微小動作を選択させています。

[dhul @DXhusni]

@Vexldiy Thank you, it surprised me how well it worked, i'm going to a attempt a harder problem like making coffee.

[dhul @DXhusni]

@CompleteSkeptic @badlogicgames You should drop a blog

[dhul @DXhusni]

Here is Jev writing code (using Qwen 8b to sample the token space.)

[Diogo Almeida @CompleteSkeptic]

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?

I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev

• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions

AFAICT the shortest path to AI-based economic revolution

[dhul @DXhusni]

@mitchellh @superlogical I can already see what you mean by a multiplexer for everything. It looks primary designed for agents to a) hook into events and b) control anything and everything remotely. It's really cool work.

[dhul @DXhusni]

@kuberdenis Congrats man, been here for all of it

[dhul @DXhusni]

@suchenzang A different class of shovel sellers

[dhul @DXhusni]

@HSVSphere What did you do?

[Lina Khan @linamkhan]

Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products. We shouldn’t let discussions about new legal regimes distract from the fact that there’s no AI exemption from laws already on the books — a point @FTC emphasized repeatedly during my tenure.

1. There is an extensive set of laws that govern dangerous and defective products. For example, releasing unvetted AI models or agents can violate consumer protection laws. Shipping flawed AI tools without implementing adequate measures to detect and stop rogue or defective AI agents can be an “unfair or deceptive” act or practice under the FTC Act (and analogous state laws). And some state AGs are already exploring holding AI firms and their CEOs criminally liable when their models participate in criminal activity.

2. Existing laws also prohibit “unfair methods of competition.” This covers instances where AI firms appropriate the competitively sensitive information of their customers, including through tracking their use of various tools. It can also cover instances where firms pursue dangerous behavior, aware that doing so may compel rivals to do the same.

As the Supreme Court has noted: “A method of competition which casts upon one's competitors the burden of the loss of business unless they will descend to a practice which they are under a powerful moral compulsion not to adopt, even though it is not criminal, was thought to involve the kind of unfairness at which the [unfair methods of competition] statute was aimed."

3. The highly concentrated and interconnected structure of these markets could be creating major risks and conflicts of interest. We had started investigating these partnerships and cross-investments across the stack (and released a preliminarily overview of some findings: https://t.co/jJ5cS3Pin3).

Both federal and state enforcers should be scrutinizing these opaque relationships and inter-dependencies. We are already seeing how these relationships could undermine accountability. For example, OpenAI could face liability given the Hugging Face incident, but Hugging Face being bought up by Nvidia means that we’re unlikely to see it file a lawsuit over this — given Nvidia’s strong incentive to see OpenAI continue full speed ahead.

4. As AI tools dramatically change the landscape of cybersecurity risks and hacks, all businesses should be doubling down on having core security protections in place. Firms that fail to invest in adequate data security measures or fix known vulnerabilities can also be breaking the law. A recent analysis showed that around 1/3 of Fortune 100 companies do not even have a way to notify them about security issues. During my @FTC tenure, we sued firms for poor data security practices and held CEOs liable when they were personally responsible.

https://t.co/nwZ5Av8fOK

https://t.co/KjRye8y9SY

5. As policymakers consider new legal regimes, we should be looking to lessons from prior efforts to govern major sectors, such as banking and other networks, platforms, and utilities. Tools like structural separations, nondiscrimination, and supervision could be key, and there’s a rich history of what works and what doesn’t. But we can and must pursue any new efforts alongside enforcing existing laws.

[𝐓𝐘 @dev_TEMITAYO]

POV: Software Engineering in 2026 be like 😭

[dhul @DXhusni]

@iamkylebalmer Le Chaton Fat

[dhul @DXhusni]

The frontier labs pacing means that China will too.

Today it’s in their best interest to do open weights and capture the markets. But that changes once the frontier labs stop releasing their best models.

Once open-source models equal the best models the frontier labs are willing to release publicly, there’s no point in going further. Handing your competition frontier weights then means giving the opposing bloc a strategic asset.

Eventually, the best models will be reserved for the nation states best scientists, elites and military. We’ll still get the Mini and Flash models.

The permanent underclass is an inevitability no matter how you look at it.

[dhul @DXhusni]

tldr;

It's so over, if you don't have an at home gpu you are cooked

[Dario Amodei @DarioAmodei]

We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so.

Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training.

You can read the full post here: https://t.co/OGyPb7yaYt

[dhul @DXhusni]

Sam to Dario

[Sam Altman @sama]

I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we've had at OpenAI in recent weeks.

Committing to having independent evaluators with employee-like access is a great idea, and we will do the same. We'll have more to share soon.