|HackYourNews - AI summaries of the top HN stories
|1. Gemma: New Open Models
Dehyped title: Google introduces new open AI models Gemma 2B and 7B for responsible development.
Gemma is a new family of open-source AI models released by Google to assist with responsible AI development. It is inspired by Google's Gemini models and was created using the same research and technology. Gemma comes in 2B and 7B parameter sizes and supports frameworks like PyTorch, TensorFlow, and JAX. It achieves state-of-the-art performance for its size and can run on devices from laptops to Google Cloud. Notably, Google also released a Responsible Generative AI Toolkit to help developers build applications safely with Gemma, which includes tools for safety classification, debugging, and accessing best practices.
impulser_: Notes the irony of OpenAI originally being seen as the most open about AI, but now Google and Meta releasing powerful open models.
blackoil: Explains that competition will be in larger models like GPT-4, and open sourcing smaller models brings PR and biases open source tools towards their own.
brainless: Points out that Google has a history of open sourcing tools for years like Chromium.
infecto: Has a different take, saying Google releases a lot but is also massive and tools like Chromium increase their stock price.
vmfunction: Sees the open sourcing as a PR stunt, and that when a company gains dominance it will likely close up what it had opened.
|2. Insecure vehicles should be banned, not security tools like the Flipper Zero
Dehyped title: Security researchers argue against proposed ban on security research tools
The document discusses a proposal by the Canadian government to ban devices used for vehicle theft, such as the Flipper Zero, through prohibiting their sale and collaboration with law enforcement. However, the authors argue this policy is misguided as it is based on outdated assumptions and will be counterproductive and impossible to enforce. They note these devices are essentially programmable radios that have legitimate uses. Additionally, banning such tools could stifle security research and economic progress. Over 200 cybersecurity professionals signed their name in agreement with these views, believing resources would be better spent improving automotive security. In an interesting point, the authors note how security research tools now help balance manufacturers to strengthen product safety.
keiferski: Argues that society has replaced ethical expectations with specific laws, and this leads to limiting people's actions through environmental constraints rather than teaching responsibility.
nulbyte: Suggests that mandating minimum security standards for vehicles fits with the goal of preventing unethical theft.
katbyte: Notes that Canada previously required immobilizers in vehicles, which helped reduce theft until newer cars had weaker security.
LunaSea: Disagrees that society assumes the worst in people and passes laws accordingly, citing examples showing optimism in areas like removing politicians from power.
eunos: States that the idea of replacing ethics with laws has existed for over 2000 years in ancient Chinese philosophy.
|3. Stephen Wolfram – 4-Hour Conversational Documentary on My Entire Arc of Life [video]
Dehyped title: Wolfram discusses his career journey from physics to Mathematica and computational thinking.
Stephen Wolfram discusses growing up with parents in academia and his early interest in science and building spacecraft designs. He shares how he started publishing physics papers as a teenager and studied at Oxford. Wolfram founded the software company Wolfram Research and created Mathematica, seeking to build an R&D focused company. An interesting point is that Wolfram has worked on large secret projects like Wolfram|Alpha over decades before publicly releasing them. Overall, Wolfram reflects on his career in science and technology spanning decades, from early academic work to founding a major software company.
Whitespace: Finds Stephen Wolfram's work often contains too much self-aggrandizement and autobiography interspersed in his writing.
zhynn: Believes Wolfram takes too much singular credit for his work and ideas without acknowledging collaborators, which makes his ideas harder to take seriously.
jack_riminton: Contrasts Wolfram with Roger Penrose and finds Penrose a more humble and interesting conversationalist compared to people who are most convinced of their own genius.
adamgordonbell: Had an enjoyable interview with Wolfram where Wolfram showed him things with Mathematica and was impressed by him, though notes Wolfram may have gotten too focused on the notation of Mathematica over the actual work.
cc101: Says Wolfram's ideas are interesting but learning about them takes too much time due to a lack of editing, and will not listen to him until he has a merciless editor.
|4. iMessage with PQ3 Cryptographic Protocol
Dehyped title: iMessage introduces post-quantum cryptography with new protocol PQ3 to secure messaging against future attacks.
The passage announces the introduction of PQ3, a new cryptographic protocol for iMessage that provides post-quantum encryption and advances the security of end-to-end messaging. PQ3 uses a hybrid design combining post-quantum and classical encryption algorithms to secure both initial key establishment and ongoing message exchanges. It introduces periodic post-quantum rekeying to limit how many past and future messages can be decrypted if a key is compromised. PQ3's security has been formally verified through multiple analyses. Notably, it is the first widely deployed messaging protocol to incorporate ongoing post-quantum ratcheting, setting a new standard for protecting conversations against both current and future quantum attacks.
tsunamifury: Criticizes Apple for not following through on their privacy values and deceiving users about encrypted messaging.
sandyarmstrong: Provides context about the post-quantum cryptography algorithms Signal and Apple chose and explains the "learning with errors" math problem.
xxmarkuski: Mentions past interesting work by David Basin modeling security protocols in Tamarin and shares a link to a demonstration.
gjsman-1000: Discusses getting stuck in an end-to-end encryption lockout and not being able to recover encrypted data like passwords and health information without a recovery contact.
mcny: Asks for clarification on whether disabling iCloud syncing of messages is sufficient to prevent backups of encrypted conversations.
|5. Pijul is a free and open source (GPL2) distributed version control system
Dehyped title: Pijul is a free open source distributed version control system based on patches that guarantees merge correctness and models conflicts as expected.
Pijul is a free and open source distributed version control system based on a theory of patches. It aims to be fast, scalable, and easy to use without compromising power or features. Independent changes can be applied in any order without changing the result or version identifier, making its workflow significantly simpler than systems like git or Mercurial. Pijul guarantees certain properties around merging, such as always preserving the order of lines. Conflicts are modeled as the standard case in Pijul - they occur between two changes and are resolved with a separate resolution change. Interestingly, Pijul also allows for "partial clones" where a user can clone only a subset of a repository and readily contribute changes back to the larger codebase.
ykonstant: Asks for a blurb highlighting the benefits of using pijul compared to other VCS and a brief comparison between pijul and git.
rkangel: Asks for a real world example of how pijul would work better than git in a scenario with multiple developers making changes.
exDM69: Provides a simplified example of how pijul handles changes made by multiple developers better than git by allowing changes to commute.
hombrebuffalo: Questions if the example only applies to adding new files or if changes could affect each other even in different files.
indigochill: Notes the comparison is between pijul and git, not github. Also wants self-hosting and IDE integration before considering a switch.
|6. Parsing: The Solved Problem That Isn't (2011)
Dehyped title: Grammar composition challenges include ambiguity, inexpressiveness, and behaviors after combining languages.
The passage discusses the challenges of composing grammars for programming languages. While parsing is theoretically trivial by combining grammars, in practice it is difficult due to issues like differing tokenization rules and ambiguity. Existing parsing algorithms like LL/LR are not well-suited for grammar composition. Scannerless parsing helps with tokenization but ambiguity remains a challenge, especially when combining grammars from different authors. PEGs are closed under composition but have their own limitations regarding expressiveness and how ordered choice affects composed grammars. An interesting point is that the author questions whether future languages may bypass parsing altogether and instead use syntax-directed editing if better parsing algorithms for grammar composition are not developed.
Comments: Unable to generate summary
|7. Launch HN: Retell AI (YC W24) – Conversational Speech API for Your LLM
No summary available
niblettc: Asked if the voice agent supports long context and can recall items from previous conversations.
yanyan_evie: Responded that adding memory and previous chat transcripts could be helpful for use cases like AI therapy sessions.
monkeydust: Tested the agent and was impressed but noted it had trouble confirming an exact date and time for a booking three weeks in the future.
|8. The Ruby on Rails Podcast Episode 508: YJIT with Maxime Chevalier-Boisvert
Dehyped title: YJIT is a just-in-time compiler for Ruby written in Rust that improves Ruby's performance.
YJIT is a just-in-time compiler for the Ruby programming language that was developed by Shopify to improve Ruby's performance. YJIT is written in the Rust language instead of Ruby. Dr. Maxime Chevalier-Boisvert came on a show to discuss YJIT, as he worked on the project. The YJIT website provides more information about the compiler and its goals of speeding up Ruby applications. Notably, YJIT uses Rust instead of Ruby to compile Ruby code, allowing it to optimize and speed up execution in a way that was previously difficult to do from within the Ruby language itself.
jacques_chester: Notes that Maxime coinvented the Basic Block Versioning approach to JITs during her studies, which is the basis for YJIT and has led to big speedups for Ruby over the past few years.
jjice: Finds it impressive that there is a Ruby on Rails podcast that has been running for 15 years, and sometimes wishes they were programming during Rails' heyday as it seems like a fine framework with a strong following.
thedanbob: Started with Rails in 2011 just before version 3.1, and sees how Rails and Ruby have matured over the years with each major release being easier to migrate to and covering more use cases out of the box.
|9. XL: An Extensible Programming Language
Dehyped title: XL programming language allows extensible syntax and semantics.
The passage discusses the history and evolution of the XL programming language over multiple versions. Early versions of XL aimed to combine the best features of other languages like C, C++, Ada, Fortran and Java. XL2 was notable as the only self-compiling version and had the most advanced handling of generics. Later versions improved the standard library and aimed to connect XL to the LLVM compiler backend. Overall, the different versions of XL show its designers' goal of creating a flexible language that combines the best capabilities of other popular languages.
netbioserror: Argues that extensible languages don't necessarily benefit from adding various syntactic forms. Suggests that as a language's ability to extend increases, the need for a single regular syntactic form also increases.
|10. AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
Dehyped title: AnyGPT introduces a unified model for multimodal tasks using discrete representations.
AnyGPT is a new multimodal language model introduced by researchers from Fudan University and Shanghai AI Laboratory. It is able to process different modalities such as text, images, speech, and music using discrete representations within the model's architecture. The researchers built a large multimodal dataset to pretrain the model on multimodal alignment. They also generated a dataset called AnyInstruct which contains over 100,000 samples of multi-turn conversations that combine various modalities. Experimental results showed that AnyGPT can facilitate any-to-any multimodal conversations while achieving performance comparable to specialized models on each individual modality. Interestingly, the researchers were able to unify multiple modalities within a single language model through discrete representations without altering the model architecture or training process.
mdrzn: Envisions a future "Generalist Multimodal Large Language Model" that can autonomously select the appropriate specialized LLM for any given task, rather than requiring users to switch between multiple LLMs. Thinks the combination of a mixture of experts approach and multimodality appears to be the way forward and is excited for the future.