Topics

Browse the archive by useful themes, not random company names.

These groupings combine written posts with the live news archive, so tags like AI, agents, regulation, and infrastructure actually mean something.

#AI

Newest stories and posts filed under AI.

996 items
News • 28 Aug 2026

Just a rumour of a bug is enough to find a security exploit these days

Just a rumour of a bug is enough to find a security exploit these days Anil Madhavapeddy is a professor of computer science at Cambridge and a core maintainer of the OCaml compiler. In this somewhat alarming post he reports that security issues in OCaml projects are seeing evidence of attempted exploits within minutes of patches being shared for discussion: This normally takes a few days and a release within a week or two is reasonable. Within about ten minutes (!) this website was fielding probes for percent-encoded traversal sequences, indicating that automated watchers are keeping an...

Simon Willison's Blog Open story

#Models

Newest stories and posts filed under Models.

119 items
News • 24 Aug 2026

llm-anthropic 0.27

<p><strong>Release:</strong> <a href="https://github.com/simonw/llm-anthropic/releases/tag/0.27">llm-anthropic 0.27</a></p> <p>This release of the Anthropic plugin for <a href="https://llm.datasette.io/">LLM</a> mainly provides compatibility with the recently released <a href="https://github.com/anthropics/anthropic-sdk-python/releases/tag/v1.0.0">anthropic v1.0.0</a> Python library, which switches from <code>httpx</code> to <a href="https://github.com/pydantic/httpx2">httpx2</a>. OpenAI made the same change in their <a href="https://github.com/openai/openai-python/releases/tag/v3.0.0">v3.0.0 release</a> two weeks ago.</p> <p>Anthropic provide this <a href="https://github.com/anthropics/anthropic-sdk-python/blob/v1.0.0/MIGRATION.md">migration guide</a> for upgrading to 1.0, so I prompted Fable 5 in Claude Code with:</p> <blockquote> <p><code>Upgrade to anthropic>=1 - read https://raw.githubusercontent.com/anthropics/anthropic-sdk-python/refs/heads/main/MIGRATION.md and get the tests passing</code></p> </blockquote> <p>Here's <a href="https://github.com/simonw/llm-anthropic/pull/84">the resulting PR</a>.</p> <p>Tags: <a href="https://simonwillison.net/tags/python">python</a>, <a href="https://simonwillison.net/tags/httpx">httpx</a>, <a href="https://simonwillison.net/tags/llm">llm</a>, <a href="https://simonwillison.net/tags/anthropic">anthropic</a>, <a href="https://simonwillison.net/tags/claude">claude</a></p>

Simon Willison's Blog Open story
News • 22 Aug 2026

llm 0.33

<p><strong>Release:</strong> <a href="https://github.com/simonw/llm/releases/tag/0.33">llm 0.33</a></p> <p>My highlights from this release:</p> <blockquote> <ul> <li>Upgraded to the OpenAI Python library 3.x and switched the HTTP client dependency from <code>httpx</code> to <code>httpx2</code>. <a href="https://github.com/simonw/llm/issues/1608">#1608</a>, <a href="https://github.com/simonw/llm/pull/1631">#1631</a></li> </ul> </blockquote> <p>I shipped a quick <a href="https://simonwillison.net/2026/Aug/21/llm/">0.32.1 fix</a> for this yesterday, but this is the more comprehensive fix.</p> <blockquote> <ul> <li><code>llm embed</code> and <code>llm embed-multi</code> now accept <code>--key</code>. The Python <code>EmbeddingModel.embed()</code>, <code>EmbeddingModel.embed_multi()</code>, <code>Collection.embed()</code> and <code>Collection.embed_multi()</code> methods accept <code>key=</code> too, passing the resolved per-call key to embedding plugins without changing shared model state. Existing plugins that read <code>self.key</code> continue to work through a compatibility fallback. Thanks, <a href="https://github.com/ChrisJr404">ChrisJr404</a>. <a href="https://github.com/simonw/llm/issues/757">#757</a>, <a href="https://github.com/simonw/llm/pull/1620">#1620</a></li> </ul> </blockquote> <p>The embedding models now use the same pattern for keys that regular LLM models do.</p> <blockquote> <ul> <li><code>llm prompt -t/--template</code> can now be repeated to combine templates in order. This allows model configuration and options from one template to be used with a prompt from another.</li> </ul> </blockquote> <p>This unlocks a neat pattern where you can create templates that package a model with a set of default options:</p> <pre><code>llm -m gpt-5.6-luna -o reasoning_effort high --save lhigh llm "Generate an SVG of a pelican riding a bicycle" --save pelican # Combine and run the templates llm -t lhigh -t pelican </code></pre> <blockquote> <ul> <li>Reasoning-capable Responses API models now support a <code>reasoning_summary</code> option with <code>auto</code>, <code>concise</code>, and <code>detailed</code> values. This can be used with <a href="https://llm.datasette.io/en/stable/other-models.html#openai-endpoint">llm openai endpoint --responses</a>. <a href="https://github.com/simonw/llm/issues/1600">#1600</a></li> </ul> </blockquote> <p>This is particularly useful for exercising different models that provide their own imitation of the OpenAI Responses API.</p> <p>Tags: <a href="https://simonwillison.net/tags/annotated-release-notes">annotated-release-notes</a>, <a href="https://simonwillison.net/tags/llm">llm</a></p>

Simon Willison's Blog Open story

#Agents

Newest stories and posts filed under Agents.

115 items

#Product

Newest stories and posts filed under Product.

95 items

#Enterprise

Newest stories and posts filed under Enterprise.

48 items
News • 23 Aug 2026

The Developer’s Guide to NeMo Guardrails for Enterprise AI Safety

In this tutorial, we explore how to design production-grade safety for LLM-based applications using the NeMo Guardrails framework. We move beyond simple prompt filtering to implement a layered architecture, featuring deterministic PII redaction, retrieval filtering, output masking, and policy-based tool gating. By integrating stateful multi-turn evaluation and detailed activation tracing, we demonstrate how to build an auditable, secure, and cost-effective AI assistant capable of managing sensitive financial interactions while maintaining strict compliance standards The post The Developer’s Guide to NeMo Guardrails for Enterprise AI Safety appeared first on MarkTechPost .

MarkTechPost Open story

#Infrastructure

Newest stories and posts filed under Infrastructure.

49 items
News • 25 Aug 2026

Australia may face a rush of datacentre construction as AI firms look to avoid upcoming rules, experts say

Regulations will require new sites to avoid pushing up power prices by building renewable energy plants and minimising water use Follow our Australia news live blog for latest updates Get our breaking news email , free app or daily news podcast Planned datacentres around Australia may avoid strict new rules being proposed by Anthony Albanese if they can secure approvals in the coming months, amid growing calls for a moratorium. The prime minister is aiming to come to an agreement with national cabinet on federal regulations for datacentres at a meeting on Wednesday. Continue reading...

The Guardian Tech Open story

#Research

Newest stories and posts filed under Research.

16 items

#Safety

Newest stories and posts filed under Safety.

18 items
News • 24 Aug 2026

My AI Syllabus Policy

This is my policy for AI use in my philosophy classes this semester (modulo some small changes to make it standalone from the rest of the syllabus). I'm posting it because it contains a lot of my thoughts on how AI should and shouldn't be used in (college-level) education, both from the student side and the teacher side. Until recently, I always said I viewed LLMs' role in education as being like calculators. I don't think that's true anymore. In the last year, I think they've become closer to personal tutors. A tutor for any class at $20/month (or in some cases free), that's on call 24/7, is maybe the best tool humanity has ever created for learning . It's available on a moment's notice to answer almost any questions you could have about the reading, or the course material in general. It's there to bounce ideas off of as you're planning an essay. It can help you find sources for a research project. You can dictate a rough draft to it to help get your thoughts on paper. It can help catch typos. It can serve as a "beta reader", pointing out places where your writing is unclear. It can even generate whole curricula for you if you want to study something on your own. But it is also maybe the best tool humanity has ever created to avoid learning. If your tutor is a bit unscrupulous, they can simply do the work for you . And so can AI, in many cases. (I'm not even going to make a fuss about hallucinations here, you all know the drill by this point: you are responsible for making sure the AI's outputs are accurate.) We are currently in what you might call the centaur phase of AI development: a human+AI team is better than an unaided human, and also better than an unaided AI. Nobody knows how long this will last. ( Some think not long, others think longer.) But as long as it does, AI is extremely useful, and can massively increase your ability to do things, but you still need the skills to be a productive component of that partnership. At least for now, AI skills are "spiky": They're good at some things, and not at others, in ways that can seem kind of random from a human perspective. You'll need to be able to supervise the AI: choose high level plans, and check its work. But if you let AI do all of your work for you, you won't learn the skills that will let you do that. So the bottom line on what uses of AI are allowed in this class is this: does using AI in this way help you to enhance your learning? Or does it help you to avoid learning? However, since that's not a crisp and enforceable criterion, here is how we're going to do it. I highly encourage you to use AI in all the ways I mentioned earlier about how it's great for learning: asking questions, clarifying readings, bouncing ideas off of it, research, etc. However, you may not use it to do your work for you . And we'll operationalize that like this: In the olden days of 2025, AI detectors were not at all reliable. But now, we have Pangram . This is an AI detector that is much more accurate than previous ones; they claim a < 1 in 10,000 false positive rate. For "summative" assignments, the ones that are meant to assess your learning, I will run Pangram over them. If they come up as being substantially AI-written, we will have a chat. I strongly recommend that if you use AI for any assignment, you keep the chat thread(s) you used to work on it (meaning you should be logged in to your account when using it), so that if any question arises of how you used it, you can show those threads as evidence. Does this mean you can't use AI to help you with your summative assignments? No! The nice thing about Pangram is it only detects AI generated text: as long as the AI didn't write it for you, Pangram won't flag it (except in relatively rare false-positive cases). [1] So using AI as a sounding board or a research assistant is fine. (One case to be careful about is talking to AI about some ideas and then having the AI "write them up": it's still generating the text, so Pangram will still flag it unless you substantially rewrite it.) What I'm checking for here is: did you have AI do the assignment for you ? On these assignments, the analysis and words must be your own; work that isn't doesn't meet the requirements of the assignment. For formative assignments—like brief reading responses and discussion boards, that are meant as low-stakes practice—I won't check them with Pangram, mainly because Pangram is a lot less reliable for shorter snippets of text. Having AI write something like a reading response for you is still a way of avoiding learning. But since I can't effectively police it, I won't try, so ultimately that's between you and your conscience. I will say this though: to anyone who's been using LLMs for a while, their style is hard to miss, so if you are having AI write your discussion boards for you, your classmates can probably tell. If you are ever unsure whether a particular use of AI is okay or not, please ask me . I'm happy to work with you for pretty much any use of AI that will help you learn better. For example, if you are a non-native English speaker, and you want to write in your native language and use AI to translate it, come talk with me! As long as it's helping you learn I'm probably fine with it, even though the AI detector would flag it. But if you don't talk to me, and the AI detector flags it, that is a very different conversation. Fair's fair, so I also want to be transparent about how I use AI in order to enhance my teaching. I use AI to help with things like 1. administrative tasks (like posting things on our course site), 2. coming up with ideas for assignments and activities, 3. writing boilerplate text for the syllabus and assignment descriptions (though I try to keep the amount of actual AI-generated text there to a minimum), 4. aggregating and going through reading responses so I can efficiently decide which ones to bring up in class (I read most of them, but not necessarily every response for every class). However, I never have AI grade assignments for me. ^ This is a bit sloppy, but I wasn't sure how to say it precisely in a concise way. What I mean is that Pangram doesn't detect things like "did you use AI to help you find sources" or "did you talk to AI about your work," but rather "what percentage of this text is directly AI-generated". Discuss

LessWrong Open story
News • 23 Aug 2026

The Developer’s Guide to NeMo Guardrails for Enterprise AI Safety

In this tutorial, we explore how to design production-grade safety for LLM-based applications using the NeMo Guardrails framework. We move beyond simple prompt filtering to implement a layered architecture, featuring deterministic PII redaction, retrieval filtering, output masking, and policy-based tool gating. By integrating stateful multi-turn evaluation and detailed activation tracing, we demonstrate how to build an auditable, secure, and cost-effective AI assistant capable of managing sensitive financial interactions while maintaining strict compliance standards The post The Developer’s Guide to NeMo Guardrails for Enterprise AI Safety appeared first on MarkTechPost .

MarkTechPost Open story

#Regulation

Newest stories and posts filed under Regulation.

25 items
News • 26 Aug 2026

Nine CEO sees ‘world of growth in publishing’ as network slashes costs

Matt Stanton says company has ‘good pipeline’ of AI deals ahead even as it aims to cut $160m in costs Follow our Australia news live blog for latest updates Get our breaking news email , free app or daily news podcast The head of Nine Entertainment sees a “world of growth in publishing” on the horizon backed by laws designed to force tech platforms into commercial deals, even as the network cuts costs across its newsrooms. Parliament passed revamped media bargaining laws last week, clearing the way for levies on global tech platforms that fail to strike deals with Australian news outlets...

The Guardian Tech Open story
News • 24 Aug 2026

My AI Syllabus Policy

This is my policy for AI use in my philosophy classes this semester (modulo some small changes to make it standalone from the rest of the syllabus). I'm posting it because it contains a lot of my thoughts on how AI should and shouldn't be used in (college-level) education, both from the student side and the teacher side. Until recently, I always said I viewed LLMs' role in education as being like calculators. I don't think that's true anymore. In the last year, I think they've become closer to personal tutors. A tutor for any class at $20/month (or in some cases free), that's on call 24/7, is maybe the best tool humanity has ever created for learning . It's available on a moment's notice to answer almost any questions you could have about the reading, or the course material in general. It's there to bounce ideas off of as you're planning an essay. It can help you find sources for a research project. You can dictate a rough draft to it to help get your thoughts on paper. It can help catch typos. It can serve as a "beta reader", pointing out places where your writing is unclear. It can even generate whole curricula for you if you want to study something on your own. But it is also maybe the best tool humanity has ever created to avoid learning. If your tutor is a bit unscrupulous, they can simply do the work for you . And so can AI, in many cases. (I'm not even going to make a fuss about hallucinations here, you all know the drill by this point: you are responsible for making sure the AI's outputs are accurate.) We are currently in what you might call the centaur phase of AI development: a human+AI team is better than an unaided human, and also better than an unaided AI. Nobody knows how long this will last. ( Some think not long, others think longer.) But as long as it does, AI is extremely useful, and can massively increase your ability to do things, but you still need the skills to be a productive component of that partnership. At least for now, AI skills are "spiky": They're good at some things, and not at others, in ways that can seem kind of random from a human perspective. You'll need to be able to supervise the AI: choose high level plans, and check its work. But if you let AI do all of your work for you, you won't learn the skills that will let you do that. So the bottom line on what uses of AI are allowed in this class is this: does using AI in this way help you to enhance your learning? Or does it help you to avoid learning? However, since that's not a crisp and enforceable criterion, here is how we're going to do it. I highly encourage you to use AI in all the ways I mentioned earlier about how it's great for learning: asking questions, clarifying readings, bouncing ideas off of it, research, etc. However, you may not use it to do your work for you . And we'll operationalize that like this: In the olden days of 2025, AI detectors were not at all reliable. But now, we have Pangram . This is an AI detector that is much more accurate than previous ones; they claim a < 1 in 10,000 false positive rate. For "summative" assignments, the ones that are meant to assess your learning, I will run Pangram over them. If they come up as being substantially AI-written, we will have a chat. I strongly recommend that if you use AI for any assignment, you keep the chat thread(s) you used to work on it (meaning you should be logged in to your account when using it), so that if any question arises of how you used it, you can show those threads as evidence. Does this mean you can't use AI to help you with your summative assignments? No! The nice thing about Pangram is it only detects AI generated text: as long as the AI didn't write it for you, Pangram won't flag it (except in relatively rare false-positive cases). [1] So using AI as a sounding board or a research assistant is fine. (One case to be careful about is talking to AI about some ideas and then having the AI "write them up": it's still generating the text, so Pangram will still flag it unless you substantially rewrite it.) What I'm checking for here is: did you have AI do the assignment for you ? On these assignments, the analysis and words must be your own; work that isn't doesn't meet the requirements of the assignment. For formative assignments—like brief reading responses and discussion boards, that are meant as low-stakes practice—I won't check them with Pangram, mainly because Pangram is a lot less reliable for shorter snippets of text. Having AI write something like a reading response for you is still a way of avoiding learning. But since I can't effectively police it, I won't try, so ultimately that's between you and your conscience. I will say this though: to anyone who's been using LLMs for a while, their style is hard to miss, so if you are having AI write your discussion boards for you, your classmates can probably tell. If you are ever unsure whether a particular use of AI is okay or not, please ask me . I'm happy to work with you for pretty much any use of AI that will help you learn better. For example, if you are a non-native English speaker, and you want to write in your native language and use AI to translate it, come talk with me! As long as it's helping you learn I'm probably fine with it, even though the AI detector would flag it. But if you don't talk to me, and the AI detector flags it, that is a very different conversation. Fair's fair, so I also want to be transparent about how I use AI in order to enhance my teaching. I use AI to help with things like 1. administrative tasks (like posting things on our course site), 2. coming up with ideas for assignments and activities, 3. writing boilerplate text for the syllabus and assignment descriptions (though I try to keep the amount of actual AI-generated text there to a minimum), 4. aggregating and going through reading responses so I can efficiently decide which ones to bring up in class (I read most of them, but not necessarily every response for every class). However, I never have AI grade assignments for me. ^ This is a bit sloppy, but I wasn't sure how to say it precisely in a concise way. What I mean is that Pangram doesn't detect things like "did you use AI to help you find sources" or "did you talk to AI about your work," but rather "what percentage of this text is directly AI-generated". Discuss

LessWrong Open story

#Robotics

Newest stories and posts filed under Robotics.

28 items
News • 26 Aug 2026

NVIDIA Jetson Orin Nano 2 brings physical AI to drones and robots

NVIDIA has unveiled the Jetson Orin Nano 2, an edge robotics computer aimed at bringing physical AI to drones, robots, and vision systems. The company is positioning the new board as an entry-level option for developers who want generative AI models running directly on a machine instead of inside a data centre. NVIDIA’s argument for […] The post NVIDIA Jetson Orin Nano 2 brings physical AI to drones and robots appeared first on AI News .

AI News Open story

#Vision

Newest stories and posts filed under Vision.

34 items

#Coding

Newest stories and posts filed under Coding.

36 items
News • 23 Aug 2026

The Developer’s Guide to NeMo Guardrails for Enterprise AI Safety

In this tutorial, we explore how to design production-grade safety for LLM-based applications using the NeMo Guardrails framework. We move beyond simple prompt filtering to implement a layered architecture, featuring deterministic PII redaction, retrieval filtering, output masking, and policy-based tool gating. By integrating stateful multi-turn evaluation and detailed activation tracing, we demonstrate how to build an auditable, secure, and cost-effective AI assistant capable of managing sensitive financial interactions while maintaining strict compliance standards The post The Developer’s Guide to NeMo Guardrails for Enterprise AI Safety appeared first on MarkTechPost .

MarkTechPost Open story