5 ms·

Today I got a feature request from another team in a call. I typed into our slack channel as a note. Someone typed @cursor and moments later the feature was imp

by tibbar 8mo ago

Today I got a feature request from another team in a call. I typed into our slack channel as a note. Someone typed @cursor and moments later the feature was implemented (correctly) and ready to merge. The tools are good! The main bottleneck right now is better scaffolding so that they can be thoroughly adopted and so that the agents can QA their own work. I see no particular reason not to think that software engineering as we know it will be massively disrupted in the next few years, and probably other industries close behind.

JohnMakin 8mo ago
It really doesn't matter how "good" these tools feel, or whatever vague metric you want - they hemorrhage cash at a rate perhaps not seen in human history. In other words, that usage you like is costing them tons of money - the bet is that energy/compute will become vastly cheaper in a matter of a couple of years (extremely unlikely), or they find other ways to monetize that don't absolutely destroy the utility of their product (ads, an area we have seen google flop in spectacularly). And even say the latter strategy works - ads are driven by consumption. If you believe 100% openAI's vision of these tools replacing huge swaths of the workforce reasonably quickly, who will be left to consume? It's all nonsense, and the numbers are nonsense if you spend any real time considering it. The fact SoftBank is a major investor should be a dead giveaway.
- nfg 8mo ago
  > In other words, that usage you like is costing them tons of money Evidence? I’m sure someone will argue, but I think it’s generally accepted that inference can be done profitably at this point. The cost for equivalent capability is also plummeting.
  - JohnMakin 8mo ago
    I didn't think there would need to be more evidence than the fact they are saying they need to spend $600 billion in 4 years on $13bn revenue currently, but here we are. Here you go: https://www.wsj.com/livecoverage/stock-market-today-dow-sp-500-nasdaq-10-31-2025/card/openai-made-a-12-billion-loss-last-quarter-microsoft-results-indicate-e71BLjJA0e2XBthQZA5X https://www.wsj.com/livecoverage/stock-market-today-dow-sp-5...
    - tibbar 8mo ago
      Right, but if OpenAI wanted to stop doing research and just monetize its current models, all indications are that it would be profitable. If not, various adjustments to pricing/ads/ etc could get it there. However, it has no reason to do this, and like all the other labs is going insanely into debt to develop more models. I'm not saying that it's necessarily going to work out, but they're far from the first company to prioritize growth over profitability
      - zippothrowaway 8mo ago
        Nope. The only "all indications" are that they say so. They may be making a profit on API usage, but even that is very suspect - compare against how much it actually costs to rent a rack of B200s from Microsoft. But for the millions of people using Codex/Claude Code/Copilot, the costs of $20-$30-$200 clearly don't compare to the actual cost of inference.
      - mike_hearn 8mo ago
        This meme needs to go in the bin. Loss making companies love inventing strange new accounting metrics, which is one reason public companies are forced to report in standardized ways. There's no such thing as "profitable inference". A company is either profitable or it isn't. Let's for a second assume all the labs somehow manage to form a secret OPEC-style cartel that agrees to slow training to a halt, and nobody notices or investigates. This is already hard to imagine with the amount of scrutiny they're under and given that China views this as a military priority. But let's pretend they manage it. These firms also have lots of other costs: • Staffing and comp! That's huge! • User subsidies to allow flat rate plans • Support (including abuse control and handling the escalations from their support bots) • Marketing • Legal fees and data licensing • Corporate/enterprise sales, which is expensive as hell even though it's often worth it • Debt servicing (!!) • Generating returns for investors Inferencing margins have to cover all of those, even if progress stops tomorrow and the RoI to investors has to be likewise very large, so margins can't be trivial. Yet what these firms have said about their margins is very ambiguous. As they're arriving at this statement by excluding major cost components like training, it's not clear what they think the cost of inferencing actually is. Are they excluding other things too like hw depreciation and upgrades? Are they excluding the cost of the corporate sales/support infrastructure around the inferencing?
- df2dd 8mo ago
  Indeed. Many of the posts I see on here are hilarious. Have any of you tried re-producing an identical output, given an identical set of inputs? It simply doesn't happen. Its like a lottery. This lack of reproducibility is a huge problem and limits how far the thing can go.
  - tibbar 8mo ago
    Determinism in agents is a complex topic because there are several different layers of abstraction, each of which may introduce its own non-determinism. But yeah, it is going to be difficult to induce determinism in a commercial coding agent, for reasons discussed below. However, we can start by claiming that non-determinism is not necessarily a bad thing - non-greedy token sampling helps prevent certain degenerate/repetitive states and tends to produce overall higher quality responses [0]. I would also observe that part of the yin-yang of working with the agents is letting go of the idea that one is working with a "compiler" and thinking of it more as a promising but fallible collaborator. With that out of the way, what leads to non-determinism? The classic explanation is the sampling strategy used to select the next token from the LLM. As mentioned above, there are incentives to use a non-zero temperature for this, which means that most LLM APIs are intentionally non-deterministic by default. And, even at temperature zero LLMs are not 100% deterministic [1]. But it's usually pretty close; I am running a local LLM as we speak with greedy sampling and the result is predictably the same each time. Proprietary reasoning models are another layer of abstraction that may not even offer temperature as knob anymore[2]. I think Claude still offers it, but it doesn't guarantee 100% determinism at temperature 0 either. [3] Finally, an agentic tool loop may encounter different results from run to run via tool calls -- it's pretty hard to force a truly reproducible environment from run to run. So, yeah, at best you could get something that is "mostly" deterministic if you coded up your own coding agent that focused on using models that support temperature and always forced it to zero, while carefully ensuring that your environment has not changed from run to run. And this would, unfortunately, probably produce worse output than a non-deterministic model. [0] https://arxiv.org/abs/2007.14966 https://arxiv.org/abs/2007.14966 [1] https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/ https://thinkingmachines.ai/blog/defeating-nondeterminism-in... [2] https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/reasoning?view=foundry-classic&tabs=csharp%2Cgpt-5 https://learn.microsoft.com/en-us/azure/ai-foundry/openai/ho... [3] https://platform.claude.com/docs/en/about-claude/glossary https://platform.claude.com/docs/en/about-claude/glossary
javascriptfan69 8mo ago
What was the feature and what was the note?
- tibbar 8mo ago
  It was a modest update to a UX ... certainly nothing world-changing. (It's also had success with some backend performance refactors, but this particular change was all frontend.) The note was basically just a transcription of what I was asked to do, and did not provide any technical hints as to how to go about the work. The agent figured out what codebase, application, and file to modify and made the correct edit.
  - javascriptfan69 8mo ago
    That's pretty neat! Thanks for elaborating.
nemooperans 8mo ago
[flagged]
- tibbar 8mo ago
  Respectfully, was this comment AI generated? It has all the signs. And scaffolding does matter a lot, but mostly because the models just got a lot better and the corresponding scaffolding for long running tasks hasn't really caught up yet.
  - nemooperans 8mo ago
    [flagged]
- rodonn 7mo ago
  There's been a huge amount of improvement in coding agent effectiveness since they ran that experiment. In a more recent follow up experiment, METR found 20% speed up from AI assistance and says they believe that is likely an underestimate of the impact. https://metr.org/blog/2026-02-24-uplift-update/ https://metr.org/blog/2026-02-24-uplift-update/ They are working on making a new measurement approach that will be more accurate.
tapoxi 8mo ago
Yeah but was Cursor using Claude? What's the moat that any of these companies have that prevents me from using another LLM?

by tibbar 8mo ago

JohnMakin 8mo ago

It really doesn't matter how "good" these tools feel, or whatever vague metric you want - they hemorrhage cash at a rate perhaps not seen in human history. In other words, that usage you like is costing them tons of money - the bet is that energy/compute will become vastly cheaper in a matter of a couple of years (extremely unlikely), or they find other ways to monetize that don't absolutely destroy the utility of their product (ads, an area we have seen google flop in spectacularly). And even say the latter strategy works - ads are driven by consumption. If you believe 100% openAI's vision of these tools replacing huge swaths of the workforce reasonably quickly, who will be left to consume? It's all nonsense, and the numbers are nonsense if you spend any real time considering it. The fact SoftBank is a major investor should be a dead giveaway.

nfg 8mo ago
> In other words, that usage you like is costing them tons of money Evidence? I’m sure someone will argue, but I think it’s generally accepted that inference can be done profitably at this point. The cost for equivalent capability is also plummeting.
- JohnMakin 8mo ago
  I didn't think there would need to be more evidence than the fact they are saying they need to spend $600 billion in 4 years on $13bn revenue currently, but here we are. Here you go: https://www.wsj.com/livecoverage/stock-market-today-dow-sp-500-nasdaq-10-31-2025/card/openai-made-a-12-billion-loss-last-quarter-microsoft-results-indicate-e71BLjJA0e2XBthQZA5X https://www.wsj.com/livecoverage/stock-market-today-dow-sp-5...
  - tibbar 8mo ago
    Right, but if OpenAI wanted to stop doing research and just monetize its current models, all indications are that it would be profitable. If not, various adjustments to pricing/ads/ etc could get it there. However, it has no reason to do this, and like all the other labs is going insanely into debt to develop more models. I'm not saying that it's necessarily going to work out, but they're far from the first company to prioritize growth over profitability
    - zippothrowaway 8mo ago
      Nope. The only "all indications" are that they say so. They may be making a profit on API usage, but even that is very suspect - compare against how much it actually costs to rent a rack of B200s from Microsoft. But for the millions of people using Codex/Claude Code/Copilot, the costs of $20-$30-$200 clearly don't compare to the actual cost of inference.
    - mike_hearn 8mo ago
      This meme needs to go in the bin. Loss making companies love inventing strange new accounting metrics, which is one reason public companies are forced to report in standardized ways. There's no such thing as "profitable inference". A company is either profitable or it isn't. Let's for a second assume all the labs somehow manage to form a secret OPEC-style cartel that agrees to slow training to a halt, and nobody notices or investigates. This is already hard to imagine with the amount of scrutiny they're under and given that China views this as a military priority. But let's pretend they manage it. These firms also have lots of other costs: • Staffing and comp! That's huge! • User subsidies to allow flat rate plans • Support (including abuse control and handling the escalations from their support bots) • Marketing • Legal fees and data licensing • Corporate/enterprise sales, which is expensive as hell even though it's often worth it • Debt servicing (!!) • Generating returns for investors Inferencing margins have to cover all of those, even if progress stops tomorrow and the RoI to investors has to be likewise very large, so margins can't be trivial. Yet what these firms have said about their margins is very ambiguous. As they're arriving at this statement by excluding major cost components like training, it's not clear what they think the cost of inferencing actually is. Are they excluding other things too like hw depreciation and upgrades? Are they excluding the cost of the corporate sales/support infrastructure around the inferencing?
df2dd 8mo ago
Indeed. Many of the posts I see on here are hilarious. Have any of you tried re-producing an identical output, given an identical set of inputs? It simply doesn't happen. Its like a lottery. This lack of reproducibility is a huge problem and limits how far the thing can go.
- tibbar 8mo ago
  Determinism in agents is a complex topic because there are several different layers of abstraction, each of which may introduce its own non-determinism. But yeah, it is going to be difficult to induce determinism in a commercial coding agent, for reasons discussed below. However, we can start by claiming that non-determinism is not necessarily a bad thing - non-greedy token sampling helps prevent certain degenerate/repetitive states and tends to produce overall higher quality responses [0]. I would also observe that part of the yin-yang of working with the agents is letting go of the idea that one is working with a "compiler" and thinking of it more as a promising but fallible collaborator. With that out of the way, what leads to non-determinism? The classic explanation is the sampling strategy used to select the next token from the LLM. As mentioned above, there are incentives to use a non-zero temperature for this, which means that most LLM APIs are intentionally non-deterministic by default. And, even at temperature zero LLMs are not 100% deterministic [1]. But it's usually pretty close; I am running a local LLM as we speak with greedy sampling and the result is predictably the same each time. Proprietary reasoning models are another layer of abstraction that may not even offer temperature as knob anymore[2]. I think Claude still offers it, but it doesn't guarantee 100% determinism at temperature 0 either. [3] Finally, an agentic tool loop may encounter different results from run to run via tool calls -- it's pretty hard to force a truly reproducible environment from run to run. So, yeah, at best you could get something that is "mostly" deterministic if you coded up your own coding agent that focused on using models that support temperature and always forced it to zero, while carefully ensuring that your environment has not changed from run to run. And this would, unfortunately, probably produce worse output than a non-deterministic model. [0] https://arxiv.org/abs/2007.14966 https://arxiv.org/abs/2007.14966 [1] https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/ https://thinkingmachines.ai/blog/defeating-nondeterminism-in... [2] https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/reasoning?view=foundry-classic&tabs=csharp%2Cgpt-5 https://learn.microsoft.com/en-us/azure/ai-foundry/openai/ho... [3] https://platform.claude.com/docs/en/about-claude/glossary https://platform.claude.com/docs/en/about-claude/glossary

javascriptfan69 8mo ago

What was the feature and what was the note?

tibbar 8mo ago
It was a modest update to a UX ... certainly nothing world-changing. (It's also had success with some backend performance refactors, but this particular change was all frontend.) The note was basically just a transcription of what I was asked to do, and did not provide any technical hints as to how to go about the work. The agent figured out what codebase, application, and file to modify and made the correct edit.
- javascriptfan69 8mo ago
  That's pretty neat! Thanks for elaborating.

nemooperans 8mo ago

[flagged]

tibbar 8mo ago
Respectfully, was this comment AI generated? It has all the signs. And scaffolding does matter a lot, but mostly because the models just got a lot better and the corresponding scaffolding for long running tasks hasn't really caught up yet.
- nemooperans 8mo ago
  [flagged]
rodonn 7mo ago
There's been a huge amount of improvement in coding agent effectiveness since they ran that experiment. In a more recent follow up experiment, METR found 20% speed up from AI assistance and says they believe that is likely an underestimate of the impact. https://metr.org/blog/2026-02-24-uplift-update/ https://metr.org/blog/2026-02-24-uplift-update/ They are working on making a new measurement approach that will be more accurate.

tapoxi 8mo ago

Yeah but was Cursor using Claude? What's the moat that any of these companies have that prevents me from using another LLM?