bretpiatt

Total global wages ~$12 trillion/year. The "what you have to believe" here is that AI tool spend becomes at least 10-20% of it, my belief here is we see net slowdown in hiring as human + AI = super productivity so as businesses grow we'll continue to see increase in AI spending.

Then if inference does grow to a $2-3 trillion/year market that Anthropic is a big winner, as big as NVDA is now (they're going to be over $500 billion in revenue in calendar year 2027).

They aren't "fast-depreciating infrastructure" -- research shared by a16z team, rental rates are up across the board, even for GPUs that are 5+ years old. The demand is real right now. The proverbial tide could go out but we aren't seeing it yet.

Source: https://x.com/a16z/status/2113986385908842870

"Zooming out, GPUs are still renting ahead of a standard 6-year depreciation schedule

Rental price vs. schedule: - H200: +143% - B200: +111% - RTX PRO 6000: +73% - H100: +54% - A100: +9%

Most of the gap opened up after April, when token demand took off"

In the footer: Permit Pending is not a joke. Is this a joke to you?

Love it, I hope this becomes the most popular game on the internet so folks get to experience the Byzantine level of complexity we've made doing business in industries with physical stuff.

He initially stated in 2011: $30,000 in 2011 is worth $44,937.52 today.

New Model 3 today $36,990 so inflation adjusted already sub-$30k, and if Cybercab cones through even with inflation.

Available in agy for me as of now, I'm running my new model benmark of having it do comprehensive code audit on one of my more complex projects, it spun up 1 subagent per audit section and it took ~10 minutes per subagent on an 800k LOC, 60k complexity, ~4k file project. Will take me much longer to go through and determine the quality of the audit work.

    Antigravity CLI 1.3.4
    [My account email] (Google AI Ultra)
    Gemini 4 Argon (High)
    ~/[working folder]

  > /model
    ⎿  Model set to Gemini 4 Argon (High)

I don't know if we'll see price increases. My belief based on current market understanding is we'll see efficiency increases.

With models getting to the quality level they can perform a specific set of work at an acceptable quality level we will now begin to cost optimize that work.

We just invested in Lamb Labs from YC Summer 2026 as example company pioneering this next wave of ASICs to run a model.

We're going to see massively lower power usage and much better tokens/sec per dollar of capital expense as well. Think Moore's law but for tokens and output quality.

If agentic coding isn't dramatically changing your productivity you're getting left behind. There's a clear difference between vibe coding and AI assisted software / systems engineering from how I think about things. With AI you can be massively more productive. I do a lot of things beyond code and this is what I've built in 2026 (quick snip from scc, it has proper excludes for node_modules and other pulled in deps that I didn't build) from ~code/ on my dev box:

  ───────────────────────────────────────────────────────────────────────────────
  Language            Files       Lines    Blanks  Comments       Code Complexity
  ───────────────────────────────────────────────────────────────────────────────
  TypeScript         10,705   2,445,724   191,695    71,737  2,182,292    167,330
  JSON                1,435     604,218        58         0    604,160          0
  Rust                1,260     718,912    56,372    57,773    604,767     65,562
  Markdown            1,087     162,160    36,375         0    125,785          0
  SQL                 1,046     129,795     6,142     7,389    116,264      1,344
  Python                705     168,359    22,710    23,235    122,414     17,962
  JavaScript            657     209,397     8,824     7,587    192,986     45,383
  ───────────────────────────────────────────────────────────────────────────────
  Total              19,009   4,903,248   353,289   186,252  4,363,707    326,094
  ───────────────────────────────────────────────────────────────────────────────
  Estimated Cost to Develop (organic) $179,262,721
  Estimated Schedule Effort (organic) 98.79 months
  Estimated People Required (organic) 161.19
  ───────────────────────────────────────────────────────────────────────────────
  Processed 206,138,543 bytes, 206.139 megabytes (SI)
  ───────────────────────────────────────────────────────────────────────────────

I'm having a hard time understanding the value here based on these launch configurations and prices, what am I missing?

From what I'm experiencing, we have recursive self improvement that can find local maxima, I'm not seeing really strong evidence of unguided RSI that finds the true optimal.

Running a bunch alongside APIs for suite of internal services where domain experts are using agents to setup environments for their work. Getting them to update their LLMs to a new SDK version every time we iterate API capabilities is all the headache you'd imagine. The MCP avoids inconsistent user reports of capabilities.

The specifics, now user can ask, "Provision 200Gi PostgreSQL 18 with PostGIS 3.6 and then copy over our most recent Texas set of layers."

Behind the scene Talos, k8s, NiFi, and bunch of things go to work. Big productivity boost vs. the users needing to login to bunch of provisioning web interfaces which is the other method we publish that avoids version drift issues.

My ability to detect sarcasm is not good. From looking at funlang's profile and other comments the profile looks like a LLM generated bot.

Forums with full no verification pseudonyms seem like they have a real challenge ahead. How long until we need humanhackernews.com with public pseudonyms and a private trusted verification?

The solution exists in the financial controls world. Agent = drafter, human = approver. The challenge is very few applications are designed to allow this, Amazon's 1-click checkout is the exact opposite. Writing a proxy for each individual app you give it access to and shimming in your own line of what the agent can do and what it needs approval is a complex and brittle solution.

10 Years Ago, December 11, 2015 - Introducing Open AI -- very meta: https://karpathy.ai/hncapsule/2015-12-11/index.html#article-...

The company has changed and it seems the mission has as well.

Page 5, "The knowledge cutoff date for Gemini 3 Pro was January 2025."

Still taking nearly a year to train and run post training safety and stability tuning.

With 10x the infrastructure they could iterate much faster, I don't see AI infrastructure as a bubble, it is still a bottleneck on pace of innovation at today's active deployment level.

I'm pretty much doing that in a containerized deployment for project I'm looking to open source soon called webbin:

  Technology Stack
   Frontend: React 18 + TypeScript + Vite + TailwindCSS
   Backend: Node.js + Express + Clustering
   Database: PostgreSQL 16 with performance optimization
   Cache: Redis 7 with active defragmentation
   Security: HTTPS/TLS with container-to-container encryption
   Orchestration: Docker Compose with health checks
   Monitoring: Built-in APM and performance tracking

  Services
   webbin-frontend - React TypeScript frontend with HTTPS
   webbin-backend - Node.js API with clustering support
   webbin-postgres - PostgreSQL 16 with performance tuning
   webbin-redis - Redis 7 with advanced caching
   webbin-certbot - SSL Certificate management, openssl for dev, LE for production
   webbin-testrunner - Testbot
   webbin-nginx - Proxy

  Access Points
   Frontend: https://localhost:5173
   Backend API: https://localhost:3001
   Health Check: https://localhost:3001/api/health

I'm around 15k LOC, all built in ~80 hours of interactive prompting mostly with Claude 4.0 Sonnet, then some Gemini 2.5 Pro for more devops activities.

He's been a target numerous times https://en.m.wikipedia.org/wiki/Brian_Krebs#Career

Thankful he's willing to continue on the mission.

*slower with Sonnet 3.7 on large open source code bases where the developer is a senior member of the project core team.

https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

I believe we'll see the benefits and drawbacks of AI augmentation to humans performing various tasks will vary wildly based on the task, the way the AI is being asked to interact, and the AI model.

English football, unlike tic-tac-toe, can be thrilling and end in a draw. Possession mix, shots on goal, and more stats are useful to determine how exciting a match was from a box score.

Frankly, for me the most boring is a 2-0 win where the team scores those 2 in the first 20 to 30 minutes, swaps to a 5-4-1, and plays tiki-taka passing possession control without trying hard to advance the ball for the remaining hour of the match.