R. Saravanan (sarava.net)

Howdy Aggies (and friends of Aggies)! The Texas A&M Department of Atmospheric Sciences will be marking the 60th anniversary of its establishment at the 7pm Tuesday (Jan. 27) reception during the AMS Annual Meeting in Hilton Americas-Houston. Come join us!

@noahqk @matthewgburgess Could you give some concrete examples of this approach for mitigation? Would you just focus on costs or also benefits (which would be global for mitigation)? How do you decide if it's "cost effective"?

@RSarava @matthewgburgess There’s no perfect answer but by setting reasonable goals and developing cost effective strategies to achieving them we can do much better than fly blind.

Economists are savage to each other in seminars but often too polite to each other in public. The implication of this paper is that a lot of policy guidance from climate economists over the last 30 years was build on sand.

@noahqk @matthewgburgess I have struggled with the same question: if impact uncertainties are large, how much $$ should we be willing to spend on climate change mitigation? My glib answer would be: About as much $$ as we waste on inefficient health care in the US. We aren't anywhere close that, i think..

@matthewgburgess If someone is claiming that the SCC is $1000/ton and therefore any action is justified, your point in response about no-regrets policies is well taken. But, alternatively, if someone is claiming that the SCC is $10/ton, you might say that much more costly policies are justified

@FinbarCurtin El Niño and Global Warming both increase global T but have very different spatial signatures and likely very different economic impacts. E.g. people assume the currently predicted super El Niño is an expression of GW, but it may just be natural variability metamodel.blog/posts/big-bad-…

16/n) The methodology of Bilal and Känzig (2026) requires that 0.1°C of climate change induce the same effect as 0.1°C of warming from ENSO, solar cycles, etc. Such is the result of reducing climate (and climate change) to a single variable.

15/n) We argue that Bilal and Känzig (2026) rely on an unrealistic exclusion restriction: temperature shocks affect GDP only through temperature levels. If shocks coincide with other climate phenomena (e.g., ENSO), the effect isn’t well-identified.

Congratulations to @tamu_atmo alum Lai‑yung Ruby Leung, Ph.D. ’91, recipient of the Michael T. Halbouty Geosciences Medal! 🌎 A remarkable leader in geosciences and a proud Aggie making lasting contributions. 👏 #TAMU

- Drafted a blog post - Used an LLM to meticulously improve the argument over 4 hours. - Wow, feeling great, it’s so convincing! - Fun idea let’s ask it to argue the opposite. - LLM demolishes the entire argument and convinces me that the opposite is in fact true. - lol The LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.

@noahqk As someone who appreciates your work and who is also a subscriber to WSJ, I was taken aback by their characterization of you! I think of you as someone who takes a boldly moderate, and somewhat skeptical, stance on climate economics—a rarity.

Nobody likes this kind of whining, but I told a reporter that a constructive climate policy discussion in New York requires stakeholders to recognize tradeoffs, and this WSJ editorial took me out of context here. This kind of propaganda is so destructive to good policymaking.

@danrothenberg @SpaceKoala The warming estimate of 0.1K is for the 0.12W/m2 forcing

@danrothenberg @SpaceKoala Experiencing slightly higher global warming is the "lesser evil" compared to the serious direct health impact of breathing pollutants like SO2. With climate sensitivity of 3C and radiative forcing of 4W/m2 for doubled CO2, the implied warming is 0.1C, similar to the sunspot cycle

Reducing sulfur emissions in bunker fuel is driving at least part of this. The sad part is people on the right don't want to admit global warming is happening, and people on the left don't want to admit that this environment regulation managed to make things worse.

Can AI discover something new about turbulence physics? Yes, but only if we didn’t already know Taylor expansion! Check out our new paper in PRL. Editors' Suggestion and American Physical Society's Physics: physics.aps.org/articles/v19/s… UChicago News: news.uchicago.edu/story/scientis…

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.

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.

@ryankatzrosene @acnewsitics Indeed. Weaker cold extremes and stronger hot extremes are both robust impacts of climate change. Comprehensive models that include all important processes predict this response over most regions. science.org/doi/10.1126/sc…

I enjoy following you Alex, and agree with most of your political content. Just a slight correction here though: Anthropogenic climate change is NOT causing “Colder cold snaps”. There is *some evidence* that climate change is linked to a growing instability of the polar vortex, which can sometimes cause cold polar air to dip down to the mid latitudes of the Northern Hemisphere. However, these cold snaps brought about by a weakened or stretched polar vortex are usually regional and signify warming elsewhere (usually in the Arctic, where the cold air was displaced). Second, there is some research suggesting that when these cold snaps occur, they are warmer than they would have been without global warming. Overall, anthropogenic climate change is leading to LESS extreme cold weather.

Climate change means extremes. Hotter heat waves. Colder cold snaps. Scientists explained this decades ago. The irony of mocking climate science during a historic climate event is almost impressive.

Like @davidbessis and others, I think that Hinton is wrong. To explain why, let me tell you a brief story. About a decade ago, in 2017, I developed an automated theorem-proving framework that was ultimately integrated into Mathematica (see: youtube.com/watch?v=mMaid2…) (1/15)

> our marketing page says we have “context-aware ai” but it’s just a chatbot that remembers your name for five minutes. the sales team calls it “persistent cognitive memory.” it’s a cookie.

A number of people are talking about implications of AI to schools. I spoke about some of my thoughts to a school board earlier, some highlights: 1. You will never be able to detect the use of AI in homework. Full stop. All "detectors" of AI imo don't really work, can be defeated in various ways, and are in principle doomed to fail. You have to assume that any work done outside classroom has used AI. 2. Therefore, the majority of grading has to shift to in-class work (instead of at-home assignments), in settings where teachers can physically monitor students. The students remain motivated to learn how to solve problems without AI because they know they will be evaluated without it in class later. 3. We want students to be able to use AI, it is here to stay and it is extremely powerful, but we also don't want students to be naked in the world without it. Using the calculator as an example of a historically disruptive technology, school teaches you how to do all the basic math & arithmetic so that you can in principle do it by hand, even if calculators are pervasive and greatly speed up work in practical settings. In addition, you understand what it's doing for you, so should it give you a wrong answer (e.g. you mistyped "prompt"), you should be able to notice it, gut check it, verify it in some other way, etc. The verification ability is especially important in the case of AI, which is presently a lot more fallible in a great variety of ways compared to calculators. 4. A lot of the evaluation settings remain at teacher's discretion and involve a creative design space of no tools, cheatsheets, open book, provided AI responses, direct internet/AI access, etc. TLDR the goal is that the students are proficient in the use of AI, but can also exist without it, and imo the only way to get there is to flip classes around and move the majority of testing to in class settings.

China 🇨🇳 meteorology research launched their new high-resolution climate reanalysis for A.I. training. The new system removes reliance on Europe's ERA5 for national security concerns. United States continues to fall further behind international peers. cryptopolitan.com/china-races-to…

Banks and credit unions are using AI to screen borrowers and buying insurance against errors made by the AI tools. Shifting risk to insurers such as David Einhorn's Greenlight Re is helping them trim their regulatory capital requirements & loan more: as.ft.com/r/06d754d5-be9…