Deep Computing have started taking orders for the final product and the Preorders are shipping within the next 6 weeks. They will be shipping from China I expect, but it's a proper shop front.
And then all the heuristics you've learnt change under you and you're stuck doing 100-1000 more hours of learning with a drop in quality during that time.
KiCad sounds to me like a great target for a project based Nix Shell install.
Always have the right version for it "locked". It works well with most tools except those that save stuff in the .config folder as it messes up isolation.
If you find the nix language daunting, for basic stuff like nix shell setup its easy but also LLMs are good for it.
The pin mapping barrier was quite off-putting to me. However I've been tracking progress in the Zephyr RTOS project and the whole line is getting better support by the day
We've deployed mattermost at my company because it meets most requirements that slack did minus the SSO. Surprisingly used by some big government agencies (NASA/USAF)
Review: An anonymous "distinguished CEO and engineer" suggests if you can't complete a feature in a day, delete your progress (except for tests) and start again the next day.
The author then recounts advice he gives to juniors, which is to stash the work and rewrite it, claiming that the next day the work will be rewritten in 25% of the time and 2x quality. This is unsubstantiated though. For juniors this suggests it will help them develop their capabilities to reason about implementations of problems without needing to face a a large amount of them.
The author then gives another advice which is to ask for a solution to a problem then after the initial proposal, ask for a 24h solution. This is meant to generate "the real solution". He likens it the a path algorithm heuristic to reach your goal quicker.
Overall the methods are not well discussed in terms of pros and cons, nor substantiated with experiments.
Opinion: I think they may help some juniors who need to build up experience and may become stuck in development patterns. But they would rarely be useful to develop someone to be a senior, if all they do is chase fast implementations. In a way the post gives conflicting advice: write twice and write better, and think twice and think about the fastest way to achieve the goal, instead of engineering a problem.
The author hasn't really convinced me of these approaches, and especially the last one smells of eXtreme Go Horse.
Review: Chonkie is an MIT license project to help with chunking your sentences. It boasts fixed length, word length, sentence and semantic methods. The instructions for installing and usage are simple.
The Benchmark numbers are massaged to look really impressive but upon scrutiny the improvements are at most <1.86x compared to the leading product LangChain in a further page describing the measurements. It claims to beat it on all aspects but where it gets close, the author's library uses a warmed up version so the numbers are not comparable. The author acknowledged this but didn't change the methodology to provide a direct comparison.
The author is Bhavnick S. Minhas, an early career ML engineer with both research and industry experience and very prolific with his GitHub contributions.
Review: Mike Jones is one of the 3 members of the OIDC working group. He celebrates the publication of the spec as a publicly accessible standard (PAS) and has worked to include the erratas so that it is a complete document.
Review: The author uses this article to say why they're not writing as much as they want. They break it down into two reasons: self judgement of quality, and the quality bar set by articles and projects in their sphere of reading.
It ends with having acknowledged the issues, the author is ready to write more.
Opinion: having seen this with many friends I think the author does good to acknowledge it, but the main thing to figure out is why they're writing. To be prolific at writing does not need to imply prolific at publishing.
I've actually started to write these review style comments because far too often the articles posted here don't have substance and interesting debates happen around bad data. So I wanted to see a change and critique the content not just the general concepts behind it. I now write more without having to accept my contributions are significant. But also create a network effect where friends read my reviews instead of being swayed by the upvotes and comment sizes, or worse the algorithm.
Review: The article tries to define the staff+ role through the lens of 4 skills: technical, people, project, and product. The article then says that a Staff+ does all of them, both at architecture level and helping build up juniors in the team.
Finally it describes them as glue between different aspects of the company.
The article provides various blogs and a couple of books as reference for statements.
Now onto my opinion. The Staff that I've met in my life are more of the solitary type, the Individual Contributor class of person who have decades of experience in their expertise and supporting skills. Yes they may have the responsibility defining the work for others, but that mostly happens as an architect. They herd seniors not mentor juniors.
The article itself doesn't work hard enough to set the bar and explain to me why I should give the title to someone when in some companies these are just the responsibilities of a senior or at a stretch Principal.
With extremely rare exceptions, this has become position inflation for the sake of looking good when going to VCs.
Review: the article finds multiple instances of users saying that when sharing from the Google discover in built web frame prepends a link shortener type website allowing Google to intermediate the link.
The article speculates that it can be used for sender and receiver tracking, but also offers a positive option which would be blocking malicious shares.
Review: the document starts strong with a methodology and numbers. It covers 3 approaches: Copilot code assistance, Llama3 fine-tuning on their codebase, and RAG on documentation. The first one is the only one supported by numbers, with 27% of code suggested being accepted by developers. Although they set up a control group they fail to relate the LLM findings to it.
Fine-tuning is suggested to improve jobs like tooling upgrades but no concrete numbers are offered.
Lastly RAG on documentation. The RAG has a simple system prompt to improve uncertain responses. They're tracking meeting and support requests but don't show any results. They mention frustration with nonsensical answers but use a RL human feedback technique to improve responses. No numbers offered.
Overall a simple overview of what they tried but the strong methodological start doesn't get reflected in the numbers reported later on.
Review: The article follows a Library's choice to leave X and move to Mastodon as the former changed hands and engagement methods.
The engagements moved from local to global which aligned with their open publication ethos. The conversations were of higher quality and the network effect of a smaller platform hasn't hampered their mission.
A good article with clear explanations of the main decision points.
Review: the article covers a small but important project to the x86 emulation under RISC-V by implementing partial support for the vector instructions (Both on RVV 1.0 and 0.7.1 platforms).
It also looks at some other micro optimisations.
Review: The article covers 5 models used in a RAG setup and evaluates their performance according to tutorials given by the respective platforms. The results are overall close but larger models show small improvements.
It then evaluates the models on safety categories where some models perform better than others, with one performing overall better.
The article presents it's methodology well so it felt the results are useful to understand for specific applications.
I liked the safety methods discussion. Likely an article that I'll refer to later when making architecture decisions