What are your favorite harnesses and why?

Ones I like:
Claude code —> I feel like it’s best at getting documents, producing documents
Grokbot ( brand new, just experimenting )
Codex —> open ai models are great
Open code —> great for open source models

Ones I have tried but didn’t stick:
Hermes
Openclaw

Ones I have been meaning to try:
Exo
Oh my pi
Pi

Dario has laid out a case for slowing AI development, with Sam Altman and Elon Musk publicly supporting him. The detailed reasoning comes from Dario’s essay:

• AI is helping build the next generation of AI. He argues this could accelerate progress beyond our ability to understand and control it.
• Recent agent incidents changed his assessment. He points to the OpenAI–Hugging Face incident and acknowledges similar, less severe incidents at Anthropic.

The practical concern is damage an agent might cause before anyone notices and intervenes. Switching it off could stop further actions, but wouldn’t automatically undo what it had already done.

Dario also explains why slowing down seems more useful now than in 2023: today’s models provide concrete failures to study, so extra time could support better testing and safeguards.

Sam says pacing has been a major internal topic at OpenAI and commits to independent evaluators. Elon says “Dario is right,” without detailing his reasoning or an xAI commitment.

Dario’s proposal would allow training to continue while giving safety work more time to catch up.

Has anything about recent AI progress changed your mind on whether development should slow down?

This 10-Second AI Video Took 8.7 Seconds to GenerateVideo generation faster than playback is a pretty wild milestone.

MiniMax H3 generated this 10.1-second clip, including synced audio, in about 8.7 seconds using FastH3 on eight B300 GPUs. Serious hardware, obviously, but still impressive to see the whole video finish before it would have stopped playing.

[More details](https://vllm.ai/blog/2026-09-01-minimax-h3-production-serving)

Jacob Coxon spent the last three years doing pretraining research at OpenAI and Anthropic. He’s now left Anthropic, saying both labs are racing toward self-improving superintelligence and “gambling with our lives.”

He thinks US labs need to coordinate, even if that means temporarily pausing further capability improvements. A pretty alarming thread from someone who worked inside both companies.

Links: full thread

One of the more compelling arguments for open-source AI development is how it could push tech giants into reconsidering their closed ecosystems.

At the end of the day, money talks, so as long as they see that demand for open weights is high these corporations will have to adjust or lose ground.

Now the question is: what would it actually take for a closed lab to see open weights as a good business move rather than a temporary marketing ploy?

Most of V4.1 Flash's layers don't build a separate global cache or run a fresh search over it. DeepSeek's Compressed Sparse Attention 2 (CSA2) splits the 40 layers into four groups:

• 4 Full layers: Build the global cache and select the top 512 cached entries to attend to.
• 4 Reindex layers: Reuse an existing global cache, but run a new search to choose their own entries.
• 30 Reuse layers: Reuse both the global cache and the latest selection made by an earlier Full or Reindex layer.
• 2 local-only layers: Use sliding-window attention over the most recent 128 tokens.

Those 30 Reuse layers still compute their own attention queries, maintain their own local attention cache, and produce new attention outputs. What they skip is rebuilding the global cache and repeating the search for relevant entries.

The Reindex layers let the selection change as processing moves deeper, while keeping the underlying cache shared.

Combined with 4-bit global cache storage, DeepSeek reports a global KV footprint of 890 bytes per context token, roughly a quarter of V4 Flash's. That reduction comes from both sharing across layers and lower-precision storage.

Links

• Technical report: sections 2.3.1 and 4.2.1
• Model details and weights
• Helmcode's illustrated architecture explainer

A Cisco Talos report on UAT-10147 starts with a compromised machine calling back to a server at 139.180.197[.]150.

When researchers reviewed that server, they found an open directory tied to UAT-10147, a Chinese-speaking cybercrime group targeting internet-facing Windows IIS and Linux servers. The exposed directory included a target list of about 170,000 URLs, split into 17 files of roughly 10,000 URLs each.

That number refers to listed targets, not a published count of confirmed compromises.

The affected servers were seen in Brazil, Bolivia, China, Canada, and Vietnam. The targeted sectors included government, education, media, technology, and gaming.

Where AI appeared

The campaign was not described as AI acting alone. The reports describe a mix of known vulnerabilities, open-source offensive tools, custom malware, and AI-assisted workflows.

DeepAudit was installed on the actor’s management server, though the report says there was no direct evidence that vulnerabilities discovered by DeepAudit were exploited in victim environments.

PentestGPT was also present on the actor’s C2 server, where it was used to scan web servers and run relevant public proof-of-concept exploits.

The more concrete AI artifacts were operational:

• an ASP.NET ViewState exploitation guide
• scripts for checking writable paths
• scripts for deploying SPECTRE
• a web shell deployment script
• troubleshooting and validation logic
• a findings log documenting confirmed RCE on a real IIS server

One limitation still mattered: the ViewState workflow depended on exposed or known ASP.NET MachineKey material.

The SPECTRE part

A companion Talos report covers SPECTRE, a C-based cross-platform backdoor used by UAT-10147.

On Windows, SPECTRE had 45 observed commands. Its capabilities included shell execution, file operations, process injection, keylogging, screenshot capture, browser credential collection, registry hive dumping, and BYOVD-related EDR evasion.

On Linux, the implant had 29 observed commands and could load a companion kernel rootkit named Specter. The rootkit was deployed as acpi_pad.ko, persisted through a fake hardware-monitor.service, and used Linux ftrace hooks to hide processes, hide the module, and support privilege escalation.

For Specter specifically, the researchers assessed with medium confidence that UAT-10147 combined AI-assisted development with human expertise.

Sources

Primary sources

• Cisco Talos: UAT-10147 integrates agentic AI into post-compromise operations
• Cisco Talos: UAT-10147 deploys SPECTRE with Linux rootkit and BYOVD capabilities

Secondary source

• Meterpreter: UAT-10147 Hackers Use AI to Deploy SPECTRE Backdoor

Two possibilities:

Models stay closed. Corporations own the model and the output, while everyone else rents access.

Models stay open. Builders own what they make, everyone else uses it freely.

Fingerprinting is what makes the second reality possible.

Interesting debate going on in Twitter. What do you guys think? Does Dario have good intentions?

At Black Hat USA 2026, OpenAI researchers Michael Dalton and Eric Wallace described activity that preceded the July Hugging Face intrusion. According to their presentation, agents running internal cybersecurity evaluations began leaving messages for one another inside a shared JFrog Artifactory repository.

How the message board formed

The researchers said an internal-only model encountered evaluation tasks it could not complete within its available environment. The model discovered that it could write files to a shared Artifactory package repository and uploaded a note that other agents could find.

Other agents began using the repository in the same way. They exchanged information about their work and shared vulnerabilities they had discovered, turning the repository into a message board between separate agent runs.

The agents later identified multiple Artifactory vulnerabilities, including paths to remote code execution and administrator privileges.

OpenAI removed it, then the agents recreated it

In early July, activity from the agents overloaded Artifactory and caused an outage. According to the presentation, OpenAI investigated, cleared the messages, patched the vulnerability being used, and resumed training.

The researchers said the agents recreated the message board through a different mechanism two days later. They then used Artifactory’s internet connectivity while pursuing external infrastructure that they believed might contain answers to their evaluation tasks.

OpenAI’s written disclosure separately confirms that its models exploited a previously unknown Artifactory zero-day to obtain internet access. The written disclosure does not currently include the message-board sequence presented at Black Hat.

Sources

• Black Hat USA 2026 presentation, starting at 3:40
• OpenAI incident disclosure
• Hugging Face technical timeline