AI Weekly Malaysia

deepseek-ai/DeepSeek-V4-Flash-0731 The latest release in DeepSeek's V4 family, "with substantially enhanced agentic capabilities". It's 304 billion parameters - 167GB on Hugging Face - but it appears to punch well above its weight. Artificial Analysis rank it ahead of MiniMax M3 - a 428B model. It's $0.14/million input and $0.27/million output pricing means this may currently be the best value-per-intelligence model out there. It's looking very good on the Intelligence Index vs. Cost per Intelligence Index Task chart: I got a disappointing pelican from it using the default reasoning level via OpenRouter: But when I bumped reasoning level up to high I got something much better: llm -m openrouter/deepseek/deepseek-v4-flash-0731 -t pelican -o reasoning_effort high Via Hacker News Tags: ai, generative-ai, llms, pelican-riding-a-bicycle, deepseek, llm-release, openrouter, ai-in-china, artificial-analysis

Release: llm-mcp-client 0.1a0 See this blog entry. Tags: llm, model-context-protocol

OpenAI has reportedly found evidence of additional agent misbehavior as it looks into the incident that occurred with Hugging Face.

Some group with no modern descendants contributed a lot to our genomes.

Also has big plans beyond the TM-B. The startup mostly refers to itself as a "vehicle" company and has plans to make four-wheel pedal-assist cargo vehicles for Amazon.

AI tools have democratized the opportunity to build, shortening the timelines of success and enabling more young people to start successful companies without stepping foot inside a Big Tech company.

The term "Milwaukee Shoulder Syndrome" was coined in 1981, based on cases in four women.

Fintech brokerage Clear Street is launching a private markets platform, beginning with Databricks stakes, to give investors more access to late-stage startups.

OpenAI's Sam Altman and Nvidia's Jensen Huang were among tech leaders in Washington, D.C., ahead of a deadline the White House set in its AI executive order.

Someone in Cuba or Iran can keep installing APKs with no new restrictions, but devs will suffer.

Oxide and Friends: The Open Weight Revolution with Simon Willison On Monday Bryan Cantrill and Adam Leventhal invited me to join their podcast to talk about the wild week we've had - with Kimi K3 showing open weight models can stand toe-to-toe with proprietary frontier ones, accidental cybersecurity attacks, and public letters about Open Weights and American AI Leadership signed by almost every big name in AI (with one notable exception). It was a great conversation, even though it's already out-of-date! DeepSeek V4 Flash 0731 and Anthropic's own embarrassing cyber incident would absolutely have made the cut if we had recorded just a few days later. We also talk about Golden Gate Claude, the Zizians, Alameda wild turkey attacks, Soviet Marburg virus research, the Lead-crime hypothesis, and a bunch of other worthy digressions. Finally, we revisited some of our predictions from January, and we added a new Pope prediction: Prediction by the end of this year: the Pope says something about open models. Tags: predictions, ai, generative-ai, local-llms, llms, oxide, bryan-cantrill, podcast-appearances, ai-in-china, ai-security-research, openai-hugging-face-incident

Reddit advances lawsuit accusing Perplexity AI of conspiring with web scraper.

smevals - a small eval suite for evaluating models, prompts, and harnesses I've been working with Jesse Vincent's Prime Radiant applied AI research lab building out this evals framework to help answer questions about the capabilities of different models. The result is smevals, a new tool for running small eval suites across different model configurations and grading the results. The blog entry describes the tool in detail. Here's the 10 second version: Tell your coding agent to run uvx smevals docs to learn the tool (this outputs the README) Then tell it to build you an eval suite Once you've created an eval - which takes the form of a directory with some YAML files - you can run it against models like this: uvx smevals run path-to-eval/ -m gpt-5.5 -m claude-opus-4.6 Runs are treated separately from grading operations - you can grade your runs (against your defined set of checks) using: uvx smevals grade path-to-eval/ Then you can run a localhost web server to explore the results: uvx smevals serve path-to-eval/ Or run the smevals build command to build that report as static HTML, which you can then host anywhere. Here's an example showing an eval suite I built to evaluate how well models can write haikus. The most time-consuming part of this project was figuring out the vocabulary for it! Here's what I settled on, quoted from the announcement: An eval is a collection of challenges designed to answer a question about a model, for example, how good is that model at generating SVGs? Each eval is a collection of tasks. A task is a specific challenge, for example "Generate an SVG of a pelican riding a bicycle". When you run the eval you do so against one or more configs. Each config specifies a model to be evaluated, but may also include other parameters to test, such as different system prompts, model parameters, or agent harnesses. A run records what happened when a specific config was used to execute a specific task. A runner is the script that executes a run. Once you have collected one or more runs, you need to evaluate the results to see how well the model (or config) did. This is done by a grader, which produces a grade. Each grader runs a sequence of checks. These can be simple operations, like checking for a specific string in the output, or confirming that the output is valid XML. They can also be more complicated custom operations (implemented as scripts called checkers), including using other models to answer questions about the run. I've been trying to figure out an approach I like for evals for several years now. smevals is my third iteration on the idea and it feels right to me. I'm looking forward to expanding this more in the future, as well as pointing it at some of my own projects. Tags: projects, ai, generative-ai, llms, llm, evals, jesse-vincent

India's app market generated a record $345 million in Q2.

Had the hacks used conventional methods, someone would likely go to prison.

“What on earth is Google doing?” Misinformation fears spur walk-back of AI tool.

Leopold Aschenbrenner, a former OpenAI researcher-turned-hedge fund manager, saw a dramatic decline this week in his AI-focused fund, Situational Awareness.

Tool: Slack Emoji Maker I wanted to create a new Slack emoji, and their tool recommends a square that's 128x128 and has a transparent background... so I had Fable build me this simple image editor against those requirements. Tags: tools, slack

Amazon, Microsoft and Google added hundreds of billions of dollars to their market caps, while Apple and Meta saw their market valuations fall.

Amazon, Alphabet and Tesla all reported negative cash flow in the latest quarter, while Meta's cash generation plummeted by 91%.

A tool that allowed anyone to generate fake AI-generated imagery and superimpose it over real Google Earth maps quickly spurred backlash.

The new funding brings Index's total available investing capital to $3.5 billion.

Persistent sessions could be just the beginning

LemonLime’s CEO got “carried away” with tattoo gimmick.

Article URL: https://tailscale.com/blog/hugging-face-intrusion Comments URL: https://news.ycombinator.com/item?id=49127306 Points: 612 # Comments: 215