My views do represent the views of my employer. Hippie Orthodox Rabbi father of 5. Wannabe Co-Author: Analytics Banquet.

The Holy City תובב"א
Joined May 2009
Ye-ho-SHU-a (phonetic) retweeted
Sometimes comedy ends up writing the most realistic script. Watch and see 🎥🎬
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Diego | AI 🚀 - e/acc
@diegocabezas01Jan 29
Replying to @OpenAI
The translation I was looking for
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Ye-ho-SHU-a (phonetic) retweeted
A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.
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Replying to @melqtx
Atlas devs: “We built this from scratch.” Also Atlas: blink “chrome://settings”
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Ye-ho-SHU-a (phonetic) retweeted
😂😂😂
i can't believe this shit actually works
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Disgusting. I would not be surprised if this story blows up and they become known as 'the racist couple'.
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I'll be launching new advanced sponsor-only features to the Analytics Debugger for Apps First of them; "Headers Monkey". Add, Remove or Modify any Request or Response Headers for debugging pourposes while using the proxy. /cc @AnalyticsNinja youtube.com/watch?v=5aDu9fL7…
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Ye-ho-SHU-a (phonetic) retweeted
Sources and many more details in this article: unchartedterritories.tomaspu… More on Iran in the coming days. Like: Will there be a regime change? Follow + subscr to not miss it
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Ye-ho-SHU-a (phonetic) retweeted
Elon Musk
@elonmusk14 Jun 2025
Replying to @marklevinshow
The beams are on
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Ye-ho-SHU-a (phonetic) retweeted
BREAKING: In an epic trolling, California Governor Gavin Newsom’s office posted a video of Emperor Palpatine reciting Donald Trump’s Truth Social post about L.A. Let’s blast this to space!
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How long should you run B2B brand campaigns before seeing an improvement in awareness? Not 30 days. Not 60 days. Not even 90 days. Most B2B marketers abandon brand initiatives way too early - killing ROI before it even has a chance. Binet & Field’s "Play the Long Game" analysis found brand lift rarely appears before month 6. And not six months after a short campaign. Six months after always-on brand marketing! After six months of steady activity the median B2B campaign shows a 6–8% rise in aided awareness and a 2-4% rise in consideration. Anything shorter is basically noise. Most CMOs expect quick results because their board does. Out of ignorance. Reality check: • Aided awareness: Needs 2-3 quarters of consistent spending • Unaided awareness: 4-8 quarters to see significant shift • Preference/consideration: Only tick up after aided awareness crosses ~50%, typically month 9-15 What works best to drive brand awareness? Eight things the research confirms: 1. Always-on broad reach (not campaign bursts) 2. Distinctive assets shown quickly (LinkedIn's neurometric research shows professionals subconsciously decide whether to remember your brand almost instantly) 3. Emotion-led creative (delivers 10-20× sales impact vs. rational-only) 4. Consistent creative over years, not quarters (memory builds like compound interest) 5. Campaigns that cover category entry points - the more scenarios people see your brand in, the faster they remember you. 6. Category-level thought-leadership and events 7. Employee-advocacy amplification (extra reach at marginal cost) 8. 50/50 brand/performance split If your boss asks why leads aren't pouring in after 60 days of brand work, share this post. The ones who win are those who play the long game.
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Ye-ho-SHU-a (phonetic) retweeted
Uhhh they’re playing DOOM in DuckDB with Instant SQL now
Running DOOM in DuckDB be like:
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When #Redis was invented, @netflix still sent DVDs. Data demands have evolved dramatically since then—and so must your infrastructure. Companies like @PingCAP & @neondatabase lead the way. Read our latest blog for predictions: dragonflydb.io/blog/the-futu… #DragonflyDB
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Mike Solana
@micsolana24 Mar 2025
the good news is signal actually is secure provided you don't invite the editor-in-chief of the atlantic to your group chat
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Ye-ho-SHU-a (phonetic) retweeted
Hey $GOOG investors - an AI hallucination appeared on screen in the pre-video for Alphabet's 2024 Q4 Earnings Call Gouda does *not* make up "50 to 60 percent of the world's cheese consumption", contrary to what Gemini for Docs says on screen in a preview of Google's Super Bowl ad This hallucination is now live on this business owner's website thanks to Gemini for Docs Google can't even fact check its own investor presentations This is what all that capex is for? An AI Slop machine?
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Ye-ho-SHU-a (phonetic) retweeted
A regular Wednesday mood at ⁦@supervveek⁩ with ⁦@AnalyticsNinja⁩ 🥰🎤
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Due to many people demand, I'm launching a Merchandise Store for @DebugEmAll in 2025! It will feature T-shirts, stickers, and Ducky plushies, with all proceeds donated to charities or open-source projects.
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This is absolutely hilarious
masterclass in why the oxford comma matters from @nytimes
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😇
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The Joys and Perils of Leaving Elon Musk’s X for Bluesky “Instead, I’m locking down my account and throwing away the key. Thanks to work, I need to keep one foot in X’s door, unfortunately.” #QuitYourJob gizmodo.com/the-joys-and-per…
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