Please reply to this comment with a url if you'd like me to run a query for you. I'll prune any results that look real bad but keep the number ordering, so if you see "1, 2, 5" it just means 3 and 4 weren't great.
Here are some recent HN posts and the list of recommended reading. Caveat: the matching algorithm makes some arbitrary decisions so no two queries will necessarily be identical.
1. Machine learning adversarial attacks are a ticking time bomb - TechTalks (https://bdtechtalks.com/2020/12/16/machine-learning-adversar...)
We are still learning how to cope with adversarial machine learning. Security researchers are used to perusing code for vulnerabilities. Now they must learn to find security holes in AI systems composed of millions of numerical parameters.
3. PrivacyRaven Has Left the Nest | Trail of Bits Blog (https://blog.trailofbits.com/2020/10/08/privacyraven-has-lef...)
By Suha S. Hussain, Georgia Tech If you work on deep learning systems, check out our new tool, PrivacyRaven-it's a Python library that equips engineers and researchers with a comprehensive testing suite for simulating privacy attacks on deep learning systems. Because deep learning enables software to perform tasks without explicit programming, it's become ubiquitous in...
4. Prompt injection: What's the worst that can happen? (https://simonwillison.net/2023/Apr/14/worst-that-can-happen/)
Activity around building sophisticated applications on top of LLMs (Large Language Models) such as GPT-3/4/ChatGPT/etc is growing like wildfire right now. Many of these applications are potentially vulnerable to prompt ...
5. The big cybersecurity themes at Black Hat 2024 - and why they matter (https://www.reversinglabs.com/blog/the-big-cybersecurity-the...)
The state of supply chain security is broken, and that leaves Black Hat attendees with a sense of urgency -and lots to discuss. Here are two main themes.
1. Five Advantages of Log-Based Change Data Capture (https://debezium.io/blog/2018/07/19/advantages-of-log-based-...)
Debezium is an open source distributed platform for change data capture. Start it up, point it at your databases, and your apps can start responding to all of the inserts, updates, and deletes that other apps commit to your databases. Debezium is durable and fast, so your apps can respond quickly and never miss an event, even when things go wrong.
2. Why Data Architecture Should Guide Your Query Engine Decision (https://www.onehouse.ai/blog/why-data-architecture-should-gu...)
Choosing a specific query engine can lead you toward an inflexible data architecture. We recommend that you first establish a solid data foundation, such as the Universal Data Lakehouse, then use the query engines of your choice against a single source of truth.
4. Book Review: Designing Data-Intensive Applications | Henrik Warne's blog (https://henrikwarne.com/2019/07/27/book-review-designing-dat...)
What a great book Designing Data-Intensive Applications is! It covers databases and distributed systems in clear language, great detail and without any fluff. I particularly like that the author Martin Kleppmann knows the theory very well, but also seems to have a lot of practical experience of the types of systems he describes. There is...
5. Of Streams and Tables in Kafka and Stream Processing, Part 1 (https://www.michael-noll.com/blog/2018/04/05/of-stream-and-t...)
In this article, perhaps the first in a mini-series, I want to explain the concepts of streams and tables in stream processing and, specifically, in Apache K...
2. Fun with vectors in the Raspberry Pi 1 - Part 9 (https://thinkingeek.com/2021/08/22/raspberry-vectors-part-9/)
I think we have enough pieces of machinery working already that we can start with the most exciting part of this journey: autovectorisation!
4. Understanding SIMD: Infinite Complexity of Trivial Problems ?? | Ash's Blog (https://ashvardanian.com/posts/understanding-simd-complexity...)
This blogpost is a mirror of the original post on Modular.com. Modern CPUs have an incredible superpower: super-scalar operations, made available through single instruction, multiple data (SIMD) parallel processing...
1. Versioning limitations in .NET | Jon Skeet's coding blog (https://codeblog.jonskeet.uk/2019/06/30/versioning-limitatio...)
This is a blog post I've intended to write for a very long time. (Other blog posts in that category include a recipe for tiramisu ice cream, and "knights and allies".) It's one of those things that's grown in my mind over time, becoming harder and harder to start. However, there have been three recent...
4. Dynamic Languages are Static Languages | Existential Type (https://existentialtype.wordpress.com/2011/03/19/dynamic-lan...)
While reviewing some of the comments on my post about parallelism and concurrency, I noticed that the great fallacy about dynamic and static languages continues to hold people in its thrall. So, in the same "everything you know is wrong" spirit, let me try to set this straight: a dynamic language is a straightjacketed static
Here are some recent HN posts and the list of recommended reading. Caveat: the matching algorithm makes some arbitrary decisions so no two queries will necessarily be identical.
Novel Universal Bypass for All Major LLMs (https://hiddenlayer.com/innovation-hub/novel-universal-bypas...) HiddenLayer's latest research uncovers a universal prompt injection bypass impacting GPT-4, Claude, Gemini, and more, exposing major LLM security gaps. https://news.ycombinator.com/item?id=43793280
1. Machine learning adversarial attacks are a ticking time bomb - TechTalks (https://bdtechtalks.com/2020/12/16/machine-learning-adversar...) We are still learning how to cope with adversarial machine learning. Security researchers are used to perusing code for vulnerabilities. Now they must learn to find security holes in AI systems composed of millions of numerical parameters.
2. How ChatGPT actually works (https://www.assemblyai.com/blog/how-chatgpt-actually-works/) Learn how ChatGPT works under the hood in this easy-to-follow guide.
3. PrivacyRaven Has Left the Nest | Trail of Bits Blog (https://blog.trailofbits.com/2020/10/08/privacyraven-has-lef...) By Suha S. Hussain, Georgia Tech If you work on deep learning systems, check out our new tool, PrivacyRaven-it's a Python library that equips engineers and researchers with a comprehensive testing suite for simulating privacy attacks on deep learning systems. Because deep learning enables software to perform tasks without explicit programming, it's become ubiquitous in...
4. Prompt injection: What's the worst that can happen? (https://simonwillison.net/2023/Apr/14/worst-that-can-happen/) Activity around building sophisticated applications on top of LLMs (Large Language Models) such as GPT-3/4/ChatGPT/etc is growing like wildfire right now. Many of these applications are potentially vulnerable to prompt ...
5. The big cybersecurity themes at Black Hat 2024 - and why they matter (https://www.reversinglabs.com/blog/the-big-cybersecurity-the...) The state of supply chain security is broken, and that leaves Black Hat attendees with a sense of urgency -and lots to discuss. Here are two main themes.
What If We Could Rebuild Kafka From Scratch? - Gunnar Morling (https://www.morling.dev/blog/what-if-we-could-rebuild-kafka-...) Update April 25: This post is being discussed on Hacker News, lobste.rs, and /r/apachekafka The last few days I spent some time digging into the recently announced KIP-1150 ("Diskless Kafka")... https://news.ycombinator.com/item?id=43790420
1. Five Advantages of Log-Based Change Data Capture (https://debezium.io/blog/2018/07/19/advantages-of-log-based-...) Debezium is an open source distributed platform for change data capture. Start it up, point it at your databases, and your apps can start responding to all of the inserts, updates, and deletes that other apps commit to your databases. Debezium is durable and fast, so your apps can respond quickly and never miss an event, even when things go wrong.
2. Why Data Architecture Should Guide Your Query Engine Decision (https://www.onehouse.ai/blog/why-data-architecture-should-gu...) Choosing a specific query engine can lead you toward an inflexible data architecture. We recommend that you first establish a solid data foundation, such as the Universal Data Lakehouse, then use the query engines of your choice against a single source of truth.
3. Reimagine Apache Kafka (https://streamnative.io/blog/ursa-reimagine-apache-kafka-for...) Ursa: Reimagine Apache Kafka for the Cost-Conscious Data Streaming
4. Book Review: Designing Data-Intensive Applications | Henrik Warne's blog (https://henrikwarne.com/2019/07/27/book-review-designing-dat...) What a great book Designing Data-Intensive Applications is! It covers databases and distributed systems in clear language, great detail and without any fluff. I particularly like that the author Martin Kleppmann knows the theory very well, but also seems to have a lot of practical experience of the types of systems he describes. There is...
5. Of Streams and Tables in Kafka and Stream Processing, Part 1 (https://www.michael-noll.com/blog/2018/04/05/of-stream-and-t...) In this article, perhaps the first in a mini-series, I want to explain the concepts of streams and tables in stream processing and, specifically, in Apache K...
Three fundamental flaws of SIMD ISA:s - Bits'n'Bites (https://www.bitsnbites.eu/three-fundamental-flaws-of-simd/) https://news.ycombinator.com/item?id=43783416
1. Snell-Pym ยป Processor architecture (https://www.snell-pym.org.uk/archives/2017/03/17/processor-a...)
2. Fun with vectors in the Raspberry Pi 1 - Part 9 (https://thinkingeek.com/2021/08/22/raspberry-vectors-part-9/) I think we have enough pieces of machinery working already that we can start with the most exciting part of this journey: autovectorisation!
3. Efficiently compiling efficient query plans for modern hardware | the morning paper (https://blog.acolyer.org/2016/05/23/efficiently-compiling-ef...)
4. Understanding SIMD: Infinite Complexity of Trivial Problems ?? | Ash's Blog (https://ashvardanian.com/posts/understanding-simd-complexity...) This blogpost is a mirror of the original post on Modular.com. Modern CPUs have an incredible superpower: super-scalar operations, made available through single instruction, multiple data (SIMD) parallel processing...
5. SIMD in the GPU world - RasterGrid (https://www.rastergrid.com/blog/gpu-tech/2022/02/simd-in-the...)
Some __nonstring__ turbulence [LWN.net] (https://lwn.net/SubscriberLink/1018486/1dcd29863655cb25/) https://news.ycombinator.com/item?id=43790855
1. Versioning limitations in .NET | Jon Skeet's coding blog (https://codeblog.jonskeet.uk/2019/06/30/versioning-limitatio...) This is a blog post I've intended to write for a very long time. (Other blog posts in that category include a recipe for tiramisu ice cream, and "knights and allies".) It's one of those things that's grown in my mind over time, becoming harder and harder to start. However, there have been three recent...
2. On Hubris And Humility - Cliffle (https://cliffle.com/blog/on-hubris-and-humility/)
3. SemVer in Rust: Tooling, Breakage, and Edge Cases FOSDEM 2024 (https://predr.ag/blog/semver-in-rust-tooling-breakage-and-ed...) Why we keep accidentally breaking SemVer, and how automation can help us do better.
4. Dynamic Languages are Static Languages | Existential Type (https://existentialtype.wordpress.com/2011/03/19/dynamic-lan...) While reviewing some of the comments on my post about parallelism and concurrency, I noticed that the great fallacy about dynamic and static languages continues to hold people in its thrall. So, in the same "everything you know is wrong" spirit, let me try to set this straight: a dynamic language is a straightjacketed static
5. Gregory Szorc's Digital Home | Mercurial's Journey to and Reflections on Python 3 (https://gregoryszorc.com/blog/2020/01/13/mercurial%27s-journ...) by cosmicgadget 1 minute ago