Vladimir Pikalov @adjreality

i can't believe nobody caught this. Anthropic's entire growth marketing team was just ONE PERSON (for 10 months, confirmed) a single non-technical person ran paid search, paid social, app stores, email marketing, and SEO for the $380B company behind claude here's exactly how one human is doing the job of a full marketing team: it starts with a CSV. 1. he exports all his existing ads from his ad platforms along with their performance metrics (click-through rates, conversions, spend, etc) 2. feeds the whole file into claude code 3. and tells it to find what's underperforming. claude analyzes the data, flags the weak ads, and generates new copy variations on the spot this is where he gets clever: he then splits the work into 2 specialized sub-agents: 1. one that only writes headlines (capped at 30 characters) 2. and one that only writes descriptions (capped at 90 characters). each agent is tuned to its specific constraint so the quality is way higher than cramming both into a single prompt so now he's got hundreds of fresh headlines and descriptions. but that's just the text. he still needs the actual visual ad creative, the images and banners that go on facebook, google, etc. so he built a figma plugin that: 1. takes all those new headlines and descriptions 2. finds the ad templates in his figma files 3. and automatically swaps the copy into each one. up to 100 ready-to-publish ad variations generated at half a second per batch. what used to take hours of duplicating frames and copy-pasting text by hand so now the ads are live. the next question is which ones are actually working. for that he built an MCP server (basically a custom integration that lets claude talk directly to external tools) connected to the meta ads API. so he can ask claude things like: • "which ads had the best conversion rate this week" • or "where am i wasting spend" and get real answers from live campaign data without ever opening the meta ads dashboard and the part that ties it all together and closes the loop: he set up a memory system that logs every hypothesis and experiment result across ad iterations. so when he goes back to step one and generates the next batch of variations... claude automatically pulls in what worked and what didn't from all previous rounds. the system literally gets smarter every cycle. that kind of systematic experimentation across hundreds of ads would normally need a dedicated analytics person just to track the numbers from the doc: ad creation went from 2 hours to 15 minutes. 10x more creative output. and he's now testing more variations across more channels than most full marketing teams a $380 billion company. and their entire growth marketing operation (not GTM) = just one person and claude code lol truly unbelievable

@byteprophetx Agree! The question is how soon AI tools incorporate that part as well.

👍 Awesome explanation of vibe coding vs. spec-driven dev. ⁉️What would be the new name when vibe coding tools incorporate the spec creation step and phase it out? Your bets😀 1) Right coding 2) Deep coding 3) No coding youtube.com/watch?v=mViFYT…

@GoogleAIStudio Teasing? Been looking for the Publish button since last weekend. 😅

💡Google Vids! Nice, right on time move from Google. workspace.google.com/products/vids/

This whole CORS + MCP saga… I wish I’d known to use the Proxy connection instead of Direct in the MCP Inspector 😅

💡After getting through the burden of building OAuth for MCP, I got flummoxed by a CORS error when connecting to the actual MCP in Inspector. If only I’d known the power of the AI Assistant in DevTools 🙂 - just ask it why you’re getting a CORS error. #MCP #CORS

I'm Boris and I created Claude Code. I wanted to quickly share a few tips for using Claude Code, sourced directly from the Claude Code team. The way the team uses Claude is different than how I use it. Remember: there is no one right way to use Claude Code -- everyones' setup is different. You should experiment to see what works for you!

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.

🚀 At a glance, Amazon Quick Suite is a pivot point. You’re no longer in the passenger seat of analytics - you can act now. Heads up: if your org already uses QuickSight, high chances you will use Quick Suite soon… so start learning😉 aws.amazon.com/blogs/aws/reim…

💡Any conspiracy behind this? 🙂🙃

@DeadPrompts @EcZachly I would disagree. I had to use almost all that are listed to solve real-life problems. I am curious, which ones are not really needed? I personally never used fine-tuning, compliance, or guardrails (although I would imagine these are all important for publicly facing apps).

@EcZachly I would probably move the "managing context length" to level 2, as it is important for RAG.

💡Tip Thursday - Did you know that the Rill leaderboard is a leaderboard for *every* dimension in the model?💡 The leaderboard is a very powerful part of the Rill interface. It’s a combination of a bar chart + a table. Plus if you have the period comparison selected, you can easily see changes by value and percent. Check out all the tutorials on YouTube👇 youtube.com/watch?v=aQQBFH…

"BI-as-code is becoming real. Last week, I walked through a live demo of one of these tools where: • Metrics layer, semantic model, etc, are both UI & YAML • Everything is version-controlled • GUI & YML = friendly interfaces for everyone Combine it with a modern analytics database, and you’ve got a high-speed, low-friction alternative to legacy dashboards—ideal for financial data, time series, and citizen developer workflows. This isn’t just faster. It’s reproducible, portable, and explainable." - John Wessel, Co-Host of the @DataStackShow

✨It is awesome how @cursor_ai orchestrates MCP Servers. Do manual work 100x times faster and reduce mistakes. 1️⃣ Write instructions on what to do 2️⃣ Configure MCP servers 3️⃣ Have Cursor to do the rest. Here is an example of data fixing in @HubSpot. 🤖queries for a data issue ➡️ analyze it ➡️ fix and leave a note (what, how, and why). It uses HubSpot and AWS Athena MCP (github.com/lishenxydlgzs/…) servers.

Vladimir Pikalov @adjreality

a year ago

Ever wonder what a typical context for an AI solution is comprised of? Look at the Model Context Protocol released by @AnthropicAI - it is an attempt to standardize the context. Resources, tools, and prompts should cover the needs of most AI apps. anthropic.com/news/model-con…

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Tired: elaborate docs pages for your product/service/library with fancy color palettes, branding, animations, transitions, dark mode, … Wired: one single docs .md file and a “copy to clipboard” button.

That is something... image transformation with #ChatGPT @OpenAI

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Today I was sent the following cool demo: Two AI agents on a phone call realize they’re both AI and switch to a superior audio signal ggwave

@cursor_ai now supports MCP Server! Our MCP server is equipped with a tool that can search (of course, Pinecone is involved) our Jira instance. Just give a hint to the composer (in agent mode), and it’ll fetch the essential info to complete your task.