Rishikesh Chiliveri

Engineering Manger @RippleHire. Father, Avid swimmer. SIMSR alumni. Travel lover. Don't follow @RippleHireJobs.

[Rishikesh Chiliveri @rishic1989]

@HedgieMarkets It means companies not focusing on optimizations instead they focus on competition and earnings

[Hedgie @HedgieMarkets]

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products.

My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.

This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.

Hedgie🤗

[Rishikesh Chiliveri @rishic1989]

Why cant LLMs select the model on its own and process requests based on the effort required.This will result in token savings and an approach to share context between models which is currently missing?
#LLMs #codex #claude #openai #anthropic

[Rishikesh Chiliveri @rishic1989]

@karpathy I think there should be a LLM or agent which validates all these and install packages in a secure way should come by default with OS.

[Rishikesh Chiliveri @rishic1989]

@InvestWithAkhil Names?

[Rishikesh Chiliveri @rishic1989]

@karpathy What is the current setup for running it do you have your dedicated servers?

[Rishikesh Chiliveri @rishic1989]

Didn't worked for me where yo add this ?

[Zain Kahn @heykahn]

Claude Code is only as good as your orchestration workflow.

I spent 6 months testing it so you don't have to.

[ P.S. You can get my Ultimate Claude Code guide for engineers here for free: https://t.co/Bx5ml72xWm ]

[Andrej Karpathy @karpathy]

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.

[Tech with Mak @techNmak]

This free CUDA course is worth more than most CS degrees.

12 hours that separate library users from GPU engineers.

I watched senior devs struggle with concepts taught in hour 3.

What makes it different:

No hand-waving. No "just use this library."

You build an MLP trainer FOUR times: → PyTorch (the easy way) → NumPy (getting harder) → C (now we're cooking) → CUDA (chef's kiss)

Same model. Same dataset. Four implementations.

By the end, you understand WHY PyTorch is fast.

The curriculum nobody else teaches:
➡️ GPU architecture (not just "it's parallel")
➡️ Writing kernels that don't suck
➡️ Profiling at kernel AND system level
➡️ When cuBLAS helps (and when it doesn't)
➡️ CUDA vs Triton (the comparison you need)
➡️ PyTorch extensions (actually useful ones)

Real talk:
➡️ After this course, you'll read PyTorch source code and understand it.
➡️ You'll optimize models other engineers can't touch.
➡️ You'll be the person teams hire to make things fast.

12 hours. Free. No excuses.

Who's starting this weekend?

(I will put the details in the comments.)

♻️ Repost to save someone $$$ and a lot of confusion.
✔️ You can follow @techNmak , for more insights.

[Rishikesh Chiliveri @rishic1989]

@ANI This dont comes in trending @X

[Rishikesh Chiliveri @rishic1989]

@jamescoder12 SEND

[Rishikesh Chiliveri @rishic1989]

@flipkartsupport @Flipkart It has been more than 20 days order didnt delivered. First order got replacement and second placed actual was 9 oct delivery. Worst experience with Flipkart evening adding delivery and platform fees.

[Rishikesh Chiliveri @rishic1989]

@code_Akter Send

[Rishikesh Chiliveri @rishic1989]

@HalimA60962 Send

[Rishikesh Chiliveri @rishic1989]

@AndrewBolis Send

[Rishikesh Chiliveri @rishic1989]

@BullsofMarket12 Name please

[Rishikesh Chiliveri @rishic1989]

@ibmswr HUSTLE

[Rishikesh Chiliveri @rishic1989]

@ibmswr hustle

[Rishikesh Chiliveri @rishic1989]

@clcoding AI

[Python Coding @clcoding]

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To get it, just:

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