Time Pin

LC

LLMs consume 5.4x less mobile energy than ad-supported web search

The standard AI energy debate compares server-side LLM inference to a server-side Google query. I think this misses most of what actually happens on a mobile device during a real search session. I built a parametric model of the full end-to-end mobile search session: 4G/5G radio energy, SoC rendering cost for a 2.5MB page, programmatic advertising RTB auctions running in the background, and network transmission costs for both sides. Then compared it to an equivalent LLM session. Main finding across 10,000 Monte Carlo draws: on mobile, a standard LLM session uses on average 5.4x less energy than a classic ad-supported web search session. Programmatic advertising alone accounts for up to 41% of device battery drain per session. Caveats I tried to be explicit about: - Advantage disappears on fixed Wi-Fi/fiber - Reverses for reasoning models - Parametric model, not empirical device measurement. Greenspector has offered to run terminal measurements for v2 - Jevons paradox applies SSRN working paper, not peer-reviewed. Methodology and Monte Carlo distributions fully documented in the paper. Happy to defend the assumptions. DOI: 10.2139/ssrn.6287918

AT

Atomic

Show HN: Atomic – Local-first, AI-augmented personal knowledge base

IB

I built a toy that plays grandma's stories when my daughter hugs it

This was a project I built for my daughter's first birthday present. For context, I'm a surgical resident in the UK by background and am currently taking a year out of training to study a masters in computer science. My daughter just turned one. There are two things she really loves: the first is particular soft toy that she just can't live without, and the other is a good story book. Her grandparents live hours away and I didn't want her to forget what they sound like between visits. I wanted her to hear them whenever she missed them. My parents brought my brother and I up with incredible stories and books from all sorts of cultures, many of the stories being passed down from their parents before them. I didn't want my daughter to miss out on that. Finally, I was sick of missing storytime with her when I had to leave for night shifts. I wanted her to hear my voice before she slept every night. For all these reasons, I decided to build Storyfriend. It's her favourite soft toy with a custom made speaker-module inside. I combined my surgical skills with the skills I was learning as a CS student. Along the way I dipped my toes into the world of 3D printing, CAD and electronics design. When she hugs the toy, it plays stories read by her grandparents. She can take the toy with her anywhere and hear the stories anytime she wants - it works offline and has internal storage. It meets my wife's strict no-screen rule (which is getting harder to stick to as the days go by). I've recorded some of the stories that we would read together, so that on nights when I'm working she still has me there to read her a bedtime story. The bit I'm most pleased with: grandparents don't need an app. They just call a phone number. The audio routes through my server and pushes to the toy over WiFi. My own 86-year old grandmother in a rural village in another country can do it by just making a regular call via her landline, as she has done for many years - no help needed, no apps required, no smartphones involved. Hardware is a BLE/wifi module with a MAX98357 chip and custome battery management system, all soldered together, placed in a 3D printed enclosure and placed into a compartment that I stitched into her cuddly toy. Firmware pulls new messages when connected to WiFi and stores them on an SD card. So far I've sold a few hand-made units to parents and grandparents who resonated with the project. Site: https://storyfriend.co.uk Would love feedback on the technical approach, the product itself, or anything else. Happy to answer questions about the build

AT

AthleteData

Im a triathlete and the data for my training lives in 6 apps: Garmin, Strava, WHOOP, Intervals.icu, Wahoo, Withings, Apple Health, sometimes Hevy. Every morning Id eyeball a few of them and make a call on whether to do the planned session. For the past month I have been building a thing that does this for me, and got it to the point where I use it myself every day. It OAuths into whatever platforms you connect, reconciles the activities (tbh harder than it sounds — same ride shows up in Strava, Garmin, and Wahoo with different timestamps and rounding), computes daily load and readiness, and proactively messages you over Telegram or Whatsapp when something matters. Stack is straightforward: Typescript all the way, Postgres, an agent loop running on Claude (via Bedrock) with tool access to all your data + my computed metrics: zones, CTL/ATL/TSB, power/pace curves, anomaly detection on HRV and RHR, etc Two things that were harder than expected: 1. Garmins API only exposes the last 90 days. So for anyone with Garmin as their primary device, you have to backfill from Strava and stitch the two together. Strava has full history but misses some fields (e.g. HR-based TSS only — no power). Wahoo and intervals.icu fill different gaps. The dedup pipeline is ugly and I'd welcome feedback from anyone who has solved this better. 2. Deciding when to message vs. stay silent is entirely a product problem. Too chatty -> muted. Too quiet -> feels dead. One honest caveat though: no RCT data, and Id be skeptical of anyone who claims they have it for AI coaching at this stage. I am at ~50 paying users, I personally reach out to every user to build the next iterations of the product based on feedback. Already got testimonials from Ironman world championship finishers and other pro athletes. Theres also a $9/mo MCP tier for people who would rather pipe their data into their own Claude/ChatGPT. Happy to go deep on any topic! e.g. the tool-calling architecture, or the cost-per-user question (running an agent on every athlete daily is not free, and the margins here are worth discussing).

DA

Daemons

For almost two years, we've been developing Charlie, a coding agent that is autonomous, cloud-based, and focused primarily on TypeScript development. During that time, the explosion in growth and development of LLMs and agents has surpassed even our initially very bullish prognosis. When we started Charlie, we were one of the only teams we knew fully relying on agents to build all of our code. We all know how that has gone — the world has caught up, but working with agents hasn't been all kittens and rainbows, especially for fast moving teams. The one thing we've noticed over the last 3 months is that the more you use agents, the more work they create. Dozens of pull requests means older code gets out of date quickly. Documentation drifts. Dependencies become stale. Developers are so focused on pushing out new code that this crucial work falls through the cracks. That's why we pivoted away from agents and invented what we think is the necessary next step for AI powered software development. Today, we're introducing Daemons: a new product category built for teams dealing with operational drag from agent-created output. Named after the familiar background processes from Linux, Daemons are added to your codebase by adding an .md file to your repo, and run in a set-it-and-forget-it way that will make your lives easier and accelerate any project. For teams that use Claude, Codex, Cursor, Cline, or any other agent, we think you'll really enjoy what Daemons bring to the table.