Hacker News: oldfuture

Temporal Language Models

oldfuture — Tue, 28 Apr 2026 04:29:53 +0000

Article URL: https://www.calcifercomputing.com/reports/tlm

Comments URL: https://news.ycombinator.com/item?id=47930469

Points: 2

# Comments: 0

Honey, I Shrunk the Coding Agent

oldfuture — Thu, 23 Apr 2026 06:29:58 +0000

Article URL: https://itayinbarr.substack.com/p/honey-i-shrunk-the-coding-agent

Comments URL: https://news.ycombinator.com/item?id=47872770

Points: 8

# Comments: 0

OMLX – Apple Silicon Optimized Local Inference

oldfuture — Thu, 16 Apr 2026 08:02:13 +0000

Article URL: https://omlx.ai/

Comments URL: https://news.ycombinator.com/item?id=47790062

Points: 1

# Comments: 0

DFlash: Block Diffusion for Flash Speculative Decoding

oldfuture — Thu, 16 Apr 2026 05:58:28 +0000

Article URL: https://z-lab.ai/projects/dflash/

Comments URL: https://news.ycombinator.com/item?id=47789204

Points: 3

# Comments: 0

Free Space Optical Link Utilizing a Modulated Retro-Reflector [pdf]

oldfuture — Thu, 16 Apr 2026 05:33:21 +0000

Article URL: https://ntrs.nasa.gov/api/citations/20200000354/downloads/20200000354.pdf

Comments URL: https://news.ycombinator.com/item?id=47789019

Points: 1

# Comments: 0

Mesh LLM

oldfuture — Thu, 16 Apr 2026 02:55:56 +0000

Article URL: https://github.com/Mesh-LLM/mesh-llm

Comments URL: https://news.ycombinator.com/item?id=47788109

Points: 4

# Comments: 0

Run Gemma 4 in Your Browser

oldfuture — Mon, 06 Apr 2026 01:29:58 +0000

Article URL: https://huggingface.co/spaces/webml-community/Gemma-4-WebGPU

Comments URL: https://news.ycombinator.com/item?id=47655878

Points: 4

# Comments: 0

Producer by Google

oldfuture — Tue, 31 Mar 2026 19:53:35 +0000

Article URL: https://www.producer.ai

Comments URL: https://news.ycombinator.com/item?id=47592567

Points: 2

# Comments: 0

New comment by oldfuture in "Building FireStriker: Making Civic Tech Free"

oldfuture — Sat, 28 Mar 2026 05:47:17 +0000

The word "fake" draws attention but I think the article obscures two real problems:

Training is missing from the analysis entirely (as someone else noted)

Inference water use is indeed minimal per prompt no argument there, but training the old GPT-3 consumed roughly 5.4 million liters of water. LLaMA 3: ~22 million. These are huge events, happening multiple times a year across the industry, folding them into national averages seems like the statistical simplification he article criticizes everyone else for doing…

"Small nationally" ≠ "fine locally"

The Dalles, Oregon is the clearest example. In 2012, Google used 12% of the city's water supply. Today it consumes a third, around 1.19 million gallons per day, and well a sixth data center comes online in 2026, in the same area.

The city is now pursuing a $260 million reservoir expansion into a national forest (!), where 95% of the projected new water demand will be industrial, not residential. Residents are looking at a potential 99% rate increase by 2036 to fund infrastructure that may exists primarily to serve one company. Apparently the city fought a 13-month legal battle just to keep those numbers secret, that’s like a community being reshaped around a single tenant.

Hays County, Texas residents sharing the Edwards Aquifer with incoming data centers voted to block one. Memphis is watching xAI draw 5 million gallons per day. Bloomberg found two-thirds of new U.S. data centers since 2022 are sited in high water-stress zones. Arizona have already passed ordinances capping data center water use.

This to me looks like a problem in the making, AI water use isn't a national crisis for now, but local impacts are already real, training costs are systematically underreported, and the five year trajectory in water stressed regions deserves serious attention indeed