#Toronto 👋
I mapped every catch basin, fire hydrant, and litter bin in the city.
226,818 assets. Each one has the city's own asset ID.
See a clogged drain? Tap it. Report it.
The city gets the exact asset number, not "somewhere on Queen St." The exact one.
From your couch. 15 seconds. No photo needed.
Spring flooding is coming.
Now you know where every drain is.
https://t.co/Me8fOAOLbb
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.
Such a rare moment. 🫡
Jensen Huang had dinner at a Korean fried chicken spot in Seoul last night with the Samsung and Hyundai Motor CEOs.
He was in a great mood, stood up to toast everyone, and announced that he’d cover everyone’s dinner. He told them to enjoy all the chicken and beer, then even bought extra chicken to hand out to fans waiting outside to see him.
We Have Won! No one can use ‘ORS’ on their label unless it’s a WHO-recommended formula.
This is the story of Dr. Sivaranjani Santosh, a braveheart paediatrician from Hyderabad, who fought for 8 years against sugar-rich drinks falsely marketed as ORS.
Her persistence led to FSSAI’s landmark order, protecting children and patients from misleading claims.
“These drinks had 10x the sugar WHO recommends, worsening diarrhoea and complications in millions of kids,” she explains.
This victory is not just hers, but belongs to everyone who stood with her — doctors, advocates, parents, and citizens demanding truth in labeling.
Scroll down to see how her 8-year battle changed the game for public health and children across India.
Credits : drsivaranjanionline on IG
#HealthVictory #PublicHealthIndia #ChildSafety #TruthInLabelling #FSSAI #DoctorsForChange #IndiaFightsBack
[ORS Ban India, Dr Sivaranjani Santosh, FSSAI Order, Public Health Victory, Sugar Drinks Misleading Labels]
This is your Net on $1M salary:
Gross income: $1,000,000
Federal Tax: $305,000
Provincial Tax: $191,000
CPP Contributions: $4,055.50
EI Premiums: $1,036.80
Ontario Health Premium: $900
Total Deductions: $501,992.30
Net Pay: $498,007.70
Welcome to Ontario, Canada 🇨🇦
Obsidian is now free for work.
Starting today, the Obsidian Commercial license is optional. Anyone can use Obsidian for work, for free. If Obsidian benefits your organization, you can still purchase Commercial licenses to support development.
Nothing else is changing. No account required, no ads, no tracking, no strings attached. Your data remains fully in your control, stored locally in plain text Markdown files. All features are available to you for free without limits.
Why make this change? Simplicity. The Commercial license terms were confusing and added unnecessary complexity to our pricing. Furthermore, as the Obsidian Manifesto states: "we believe that everyone should have the tools to think clearly and organize ideas effectively". This change brings us closer to that principle.
People in over 10,000 organizations use Obsidian. Many work in high-security environments, like government, cybersecurity, and finance. Some of the largest organizations in the world, including Amazon and Google, have thousands of employees using Obsidian every day. These teams rely on Obsidian to think more effectively and keep total ownership over private data.
Previously, people at companies with two or more employees were required to purchase a Commercial license to use Obsidian for work. Going forward, the Commercial license is no longer required, but remains an optional way for organizations to support Obsidian, similar to the Catalyst license for individuals.
Organizations that support Obsidian are now featured on the Obsidian Enterprise page. Your organization can be showcased by purchasing 25 licenses or more.
Along with Commercial and Catalyst support, our add-on services, Sync and Publish help Obsidian remain 100% user-supported. In the future, we hope to offer more services designed for teams. As always, these will be optional.
🫶Google and Apple have embraced ISO 21496-1, a new standard for HDR photos!
Android 15, iOS 18, iPadOS 18, macOS 15, and recent versions of Google Chrome all support images that have ISO 21496-1-compliant HDR gain maps.
Here's why that's a big deal👇
https://t.co/wpJdwuS64F
Yes @Apple, when I type 'bluetoot', it's pretty clear that I'm looking for your "Bluetooth File Exchange" app that nobody has ever used in the history of computing, not the Bluetooth settings that I open nearly every day to connect my headphones.
Reminder that the green 400-series highway running parallel to the red 401, about 8km to the north, was sold off by the previous @OntarioPCParty government for $3.1B in 1999, and that the current government would rather throw $150B in a hole than address that mistake.
@jmwilt21 They pushed a software update that bricked a working phone and asked customers to pay to get it repaired, I doubt that other premium manufacturers would care so little about customer experience.
Why has it become normal for people to have blinds closed during day flights. We are majestically flying over an incredible world , why would we not seek any moment to snatch any magnificent glimpse of it.
Ran a simple benchmark (Mandelbrot sets) between Mojo & Python. The speedup is impressive, and it benefits from Python's libraries.
• Python: 1,184ms
• Mojo: 27ms 🤯
• Python (vectorized): 240ms
• Mojo (vectorized): 2ms