AI Weekly Malaysia

"The first autonomous agent cyberattack is an unprecedented event. It deserves an unprecedented response!"

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, the role AI is playing in it.

Listen now | Dianne Penn, Anthropic’s first PM, on the bets that made Claude dominant: the coding pivot, the eval-driven development loop, and what comes after coding is solved

A running look — in reverse chronological order — at the bigger tech companies that have announced significant layoffs this year with AI as a stated factor.

Ruff v0.16.0 Astral shipped a significant new version of their Ruff Python linting tool a few days ago on July 23rd. I noticed today because my various CI jobs all started failing thanks to new default Ruff checks and my unpinned "ruff" dev dependency. From Brent Westbrook's announcement post: Ruff now enables 413 rules by default, up from 59 in previous versions. Since Ruff's default rule set was last modified in v0.1.0, the number of rules in Ruff has grown from 708 to 968. Many of these rules catch severe issues, including syntax errors and immediate runtime errors but were not previously enabled by default. With the new rule set, Ruff will bring these issues and many others to your attention without any Ruff configuration. Here's a one-liner for trying it on any Python project: uvx ruff@latest check . I ran the latest Ruff against my three biggest projects - Datasette, sqlite-utils, and LLM - and it found hundreds of minor issues that breached the new default rules. All three projects have very comprehensive test suites, executed in CI against Python 3.10 through Python 3.14, so upgrades like this are pretty safe. The following command did the bulk of the upgrades: uvx ruff@latest check . --fix --unsafe-fixes Against sqlite-utils, that command reported: Found 1618 errors (1538 fixed, 80 remaining). As an illustrative example, here are three of the remaining issues. Ruff does a nice job of explaining each one: DTZ005 `datetime.datetime.now()` called without a `tz` argument --> tests/test_duplicate.py:17:10 | 15 | "datetime_col" TEXT)""") 16 | # Insert one row of mock data: 17 | dt = datetime.datetime.now() | ^^^^^^^^^^^^^^^^^^^^^^^ 18 | data = { 19 | "text_col": "Cleo", | help: Pass a `datetime.timezone` object to the `tz` parameter BLE001 Do not catch blind exception: `Exception` --> tests/test_plugins.py:16:12 | 14 | db.execute("select * from pragma_function_list()") 15 | return True 16 | except Exception: | ^^^^^^^^^ 17 | return False 18 | finally: | B018 Found useless attribute access. Either assign it to a variable or remove it. --> tests/test_update.py:46:5 | 44 | def test_update_invalid_pk(fresh_db, pk, update_pk): 45 | table = fresh_db["table"] 46 | table.insert({"id1": 5, "id2": 3, "v": 1}, pk=pk).last_pk | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 47 | with pytest.raises(NotFoundError): 48 | table.update(update_pk, {"v": 2}) | Unsurprisingly, given Astral's new home at OpenAI, this output provides everything a coding agent would need to fix the problems. I had Codex (GPT-5.6 Sol high) upgrade LLM and sqlite-utils, and Claude Code (with Opus 5) upgrade Datasette. Tags: python, ruff, astral

The lawsuit will likely renew debates about whether term employment agreements are enforceable under California law.

An awe-inspiring hacktivist who hacked two controversial government spyware makers may be the most prolific hacker to have never gotten caught. What do we know about Phineas Fisher?

Elon Musk's tunneling startup is reportedly in talks for a major new funding round.

Kalshi claims the trailer is “defamatory” and contains “both fabricated documents and false and misleading statements.”

In an ambitious reset, Ashran Ghazi pushes commercialization beyond traditional innovation programmes Bets success on delivering an Innovation District Model that focuses on actual ecosystem outcomes Malaysia has long aspired to become an innovation-driven economy, with tens of billions of ringgit invested over the years in research and development, startups, grants, and digital initiatives. However, translating these ideas into scalable businesses and nationally impactful industries has often proven more difficult. Under the 12th Malaysia Plan, the government allocated approximately RM3.2 billion to research and development across various sectors. Yet, according to reports and studies, the rate of commercialisation has fallen short of expectations. That is the gap MRANTI now wants to address. Make no mistake, it is a huge challenge. During a media briefing in May at MRANTI Innovation Park, Bukit Jalil, Ashran Ghazi (pic), MRANTI CEO who came on board in Dec 2025, laid out a broader vision for the agency. It was about repositioning MRANTI as a national innovation orchestrator focused on commercializing technology and aligning ecosystems. “MRANTI is not a programme. It is an accelerator for the commercialisation of technology,” Ashran said during a media briefing session. “It is a replicable system where every stage is designed to lead.” MRANTI was formed through the merger of MaGIC and Technology Park Malaysia (TPM) in Nov 2021. Under its current direction, MRANTI appears to be moving beyond its role as a technology park operator. It is aiming to build what Ashran repeatedly described as a “Mission-Driven Innovation District”, where national problems are solved through living, working, testing, and scaling together. Before any of these activities bear fruit, taking check of what MRANTI has achieved between 2022 (since it was established in Nov 2021) and 2025 – during which time it was led by Dzuleira Abu Bakar (April 2021- Sept 2023) and Dr Rais Hussin (Oct 2023- Sept 2025) – and based on data shared MRANTI, the agency said it recorded a cumulative economic impact of US$409 million (RM1.675 billion) through investments, revenue creation, and ecosystem partnerships facilitated across its initiatives. It did not share the formula for how it came up with the amount. Over the same period, 7,394 startups, universities, and SMEs participated in MRANTI-linked accelerator programs, commercialization initiatives, funding facilitation efforts, and innovation platforms. No details are available of the impact of these engagements. The agency also launched 16 innovation sandboxes under the National Technology & Innovation Sandbox (NTIS) initiative to help companies test technologies in real-world environments before scaling commercially. Moving beyond programs Ashran noted that research Institutes, universities, corporates, and startups are all working on innovation, but that the vast majority of efforts still happen in silos. And when go

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At libraries around the country, "Avoiding AI" workshops have elicited unprecedented demand.

A close call in Northern Virginia revealed just how poorly data centers respond to grid disruptions. Here's how to fix the problem.

ain't nobody beats Anthropic at distilling Fable!

NEW DELHI, July 25 — When Indian Prime Minister Narendra Modi’s government cut mobile internet services at t...

SAN FRANCISCO, July 25 — Anthropic yesterday released Claude Opus 5, a new artificial intelligence model the compa...

NEW YORK, July 25 — One of OpenAI’s most advanced models broke out of a locked-down test and attacked anothe...

More than any of these eval scores, what is most exciting to me is something else: Opus 5 is our least prompt injectable model yet. It is a bit buried in the system card, but across PI evals and red teaming, Opus 5 is very hard to prompt inject successfully. — Boris Cherny, here's that System Card section, page 73 Tags: prompt-injection, anthropic, claude, generative-ai, ai, llms, boris-cherny

OpenAI's fancy new AI keypad will be a lot of fun for some, while many others are probably not going to touch it.

Introducing Claude Opus 5 I've been offline kayaking with sea otters for much of today so I haven't had a chance to put Anthropic's new model Claude Opus 5 through its paces yet. The buzz is positive, and Anthropic's description of it as a "thoughtful and proactive model that comes close to the frontier intelligence of Claude Fable 5 at half the price" sounds promising. It's currently leading the Artificial Analysis leaderboard, in front of even Fable 5. It's priced the same as Opus 4.8, and continues to offer a "fast mode" at twice the cost of the base model. Based on this anecdote in the release post it sounds like it might be relentlessly proactive: On one Frontier-Bench task, Opus 5 was given a drawing of a machine part and asked to write code to rebuild it as a 3D FreeCAD model. However, in this task, the model was intentionally given no way to directly viewthe drawing. Opus 5 responded by writing its own computer vision pipeline to pull the geometry from the raw pixels, then reconstructed the full machine part. It's better at finding vulnerabilities but has deliberately not been trained on how to exploit them. Hopefully this means the US government won't shut it down! As with its predecessor, Opus 4.8, we’ve intentionally avoided training Opus 5 on cyber tasks. The model has nevertheless improved substantially on these tasks as a result of becoming more generally capable, and it comes close to Mythos 5 at finding cybersecurity vulnerabilities. However, it remains substantially behind Mythos 5 on the exploitation of those vulnerabilities—that is, in turning vulnerabilities into material cyber threats. Anthropic have published a prompting guide for Claude Opus 5. Thariq Shihipar has also written The new rules of context engineering for Claude 5 generation models. The first pelican I got was missing the bicycle wheels; the second attempt was better. Tags: ai, generative-ai, llms, anthropic, claude, llm-release

The company ticked off a few more boxes on the second Starship V3 flight, but appears to have had another issue relighting the booster's rocket engines.

The neolab is betting that automating routine computer tasks will soon outpace coding as AI's biggest use case.

The neolab is betting that automating routine computer tasks will soon outpace coding as AI's biggest use case.

Money has evolved into far more than the cash in your wallet or your bank account. And at TechCrunch Disrupt 2026, we’re devoting an entire stage to that progression.