deepanshu @dy__dev

@claudeai Claude Opus is the best!

@MistralAI @Airbus @BMW @EDFofficiel Partnership with Airbus is interesting because, based on fatal accidents per million departures across large wide-body aircraft, Airbus ranks #1 as the safest wide-body manufacturer.

LLMs have reinforced my love for technical books. AI is great for learning, but it can't replicate an expert walking you through a topic step-by-step. There's a structure and depth to books that LLMs just can't match. Deep technical learning >>> a quick, shallow understanding.

VIDEO | Jaipur, Rajasthan: The Forest Department has implemented special measures at Jaipur's Nahargarh Biological Park to protect animals from intense heat. Jumbo coolers and sprinklers have been installed throughout the park for the wildlife, and their diets have been modified. #Jaipur #JaipurZoo #Heatwave (Full video available on PTI Videos - ptivideos.com)

Claude @claudeai has been really crushing it lately with their interactive widgets inside Claude chat.

Vercel has released react-best-practices - an #opensource repository featuring 40+ performance optimization rules for #React & #NextJS apps. It is structured for use by AI coding agents & LLMs - while still being highly practical for human developers. ⇨ bit.ly/4u4HEju

@rakyll I don't know if this is relevant, but when ENIAC computer was invented, it was advertised to have completed a whole year worth of a job in just 2 hours. x.com/i/status/19274…

deepanshu @dy__dev

a year ago

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You can NOT request an AI to give right answers only. For example, I asked AI for applying mathematical operations on two numbers such that result is 100 and using only numbers with unique digits and no zeroes. I further told it to give only correct responses.

Anthropic Study: AI Coding Assistance Reduces Developer Skill Mastery by 17% bit.ly/46RNURK by @SteefJan

@loganthorneloe Cannot dm

It’s easier than ever to write code… And yet the hard parts of software engineering are still *very hard* Don’t get discouraged by the hype. There’s still so much to learn and build.

Well genAI really fails at the task of tossing a coin and reporting the results. It consistently gave the output 5 heads and 5 tails when asked to toss a coin 10 times. BTW it also fails at generating an image with a light switch turned on. x.com/i/status/19440…

deepanshu @dy__dev

11 months ago

You probably shouldn't ask an AI to toss a coin. I asked multiple AIs to repeat the experiment of tossing a coin 10 times, and the results are quite amusing. Literally all of them recorded 5 heads and 5 tails.

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@GergelyOrosz I'm a real person 🙌

Going out on the road yesterday while the cricket match was on, reminded me of Corona times. #INDvsPAK

NASA writes mission-critical flight software in C. And the rules are absolutely INSANE. > No recursion. Ever. > Every loop must have a provable upper bound. > No dynamic memory allocation after initialization. > Max ~60 lines per function. > Minimum 2 assertions per function. > Every return value must be checked. > Zero compiler warnings allowed. > Daily static analysis. Zero warnings there too. > No function pointers. > Restricted pointer dereferencing. This is how they write code at NASA / JPL for mission-critical systems.

POV: You invented the programming language, Python. Happy birthday, Guido van Rossum! Van Rossum is the creator of Python and one of the most influential figures in modern computing. His vision for a language that emphasized readability, simplicity, and community fundamentally changed how software is written and taught, lowering barriers to entry while scaling to power everything from scientific research to artificial intelligence. Python has become the standard language for the modern era of Artificial Intelligence and Machine Learning. Its ability to act as a "glue language" (easily connecting with high-performance code written in C/C++) allows developers to build powerful libraries. Python has helped the field of data science evolve from theory to practical application.

Vibe-coding is not the same as AI-Assisted engineering. A recent Reddit post described how a FAANG team uses AI and it sparked an important conversation about semantics: "vibe coding" and professional "AI-assisted engineering". While the post was framed as an example of the former, the process it detailed - complete with technical design documents, stringent code reviews, and test-driven development - is a clear example of the latter imo. This distinction is critical because conflating the two risks both devaluing the discipline of engineering and giving newcomers a dangerously incomplete picture of what it takes to build robust, production-ready software. As a reminder: "vibe coding" is about fully giving in to the creative flow with an AI (high-level prompting), essentially forgetting the code exists. It involves accepting AI suggestions without deep review and focusing on rapid, iterative experimentation, making it ideal for prototypes, MVPs, learning, and what Karpathy calls "throwaway weekend projects." This approach is a powerful way for developers to build intuition and for beginners to flatten the steep learning curve of programming. It prioritizes speed and exploration over the correctness and maintainability required for professional applications. There is a spectrum between vibe coding and doing it with a little more planning, spec-driven development, including enough context etc and what is AI-assisted engineering across the software development lifecycle. In stark contrast to the post, the process described in the Reddit post is a methodical integration of AI into a mature software development lifecycle. This is "AI-assisted engineering," where AI acts as a powerful collaborator, not a replacement for engineering principles. In this model, developers use AI as a "force multiplier" to handle tasks like generating boilerplate code or writing initial test cases, but always within a structured framework. Crucially, the big difference here is the human engineer remains firmly in control, responsible for the architecture, reviewing and understanding every line of AI-generated code, and ensuring the final product is secure, scalable, and maintainable. The 30% increase in development speed mentioned in the post is a result of augmenting a solid process, not abandoning it. For engineers, labeling disciplined, AI-augmented workflows as "vibe coding" misrepresents the skill and rigor involved. For those new to the field, it creates the false and risky impression that one can simply prompt their way to a viable product without understanding the underlying code or engineering fundamentals. If you're looking to do this right, start with a solid design, subject everything to rigorous human review, and treat AI as an incredibly powerful tool in your engineering toolkit - not as a magic wand that replaces the craft itself.

Don't think of LLMs as entities but as simulators. For example, when exploring a topic, don't ask: "What do you think about xyz"? There is no "you". Next time try: "What would be a good group of people to explore xyz? What would they say?" The LLM can channel/simulate many perspectives but it hasn't "thought about" xyz for a while and over time and formed its own opinions in the way we're used to. If you force it via the use of "you", it will give you something by adopting a personality embedding vector implied by the statistics of its finetuning data and then simulate that. It's fine to do, but there is a lot less mystique to it than I find people naively attribute to "asking an AI".

Using gen AI for a long time, and only once did the AI committed a spelling mistake where it added an extra letter to the variable name. It happened when I asked it to generate a simple 3d model and it generated more than 1000 lines of code.

A "crowded" market is not really crowded if it's just a bunch of crappy options. Music players were crowded before iPod. Earbuds were crowded before the Airpods. (Inspired by meeting a founder today who's building in a "crowded" market and steamrolling everyone.)