I’ve worked with flip phones for years, and I think this is the first one I’ve seen with Sailfish! I didn’t see the specs, but it’s got to be comparable to a budget Android phone to run that well. I’m curious to see more of the UX, performance, and security. How does it come to an RTOS phone running Mocor, or a smart feature phone running KaiOS?
What happened to kit homes and plan books? Rather that bring down the cost of custom design, can we bring down the cost of partially pre-fabricated, modern code-compliant homes that still resemble something traditional (not a shipping container home)?
That said, I think this is neat because I’m buying a condo and knowing what’s even possible (physically, allowed by code, etc) seems more art than science. Getting answers usually involves bringing someone on-site, and almost nothing can be DIY’d anymore (either due to permits, HOA, complexity, etc)
I love SQLite and have ran it even on networked drives with queued writes for read-heavy applications. It’s an incredibly robust piece of software that’s often cost me pennies per month to serve 100k+ monthly users. But there’s definitely a time and place for solid, dedicated database servers like Postgres.
Subjectively, AFM isn’t even close to Qwen. It’s one of the weakest models I’ve used. I’m not even sure how many people have Apple Intelligence enabled. But I agree, there must be a huge onboarding win long-term using (and adapting) a model that’s already optimized for your machine. I’ve learned how to navigate most of its shortcomings, but it’s not the most pleasant to work with.
Sent a LinkedIn request. I’m building a language-specific coding agent using Apple Intelligence with custom adapters. It’s more a proof-of-concept at this point, but basic functionality actually works! The 4K context window is brutal, but there’s a variety of techniques to work around it. Tighter feedback loops, linters, LSPs, and other tools to vet generated code. Plus mechanisms for on-device or web-based API discovery. My hypothesis is if all this can work “well enough” for one language/ runtime, it could be adapted for N languages/ runtimes.
That’s awesome! I’ve got a similar project for macOS/ iOS using the Apple Intelligence models and on-device STT Transcriber APIs. Do you think it the models you’re using could be quantized more that they could be downloaded on first run using Background Assets? Maybe we’re not there yet, but I’m interested in a better, local Siri like this with some sort of “agentic lite” capabilities.
Neat! I’ve actually been building with AFM, including training some LoRA adapters to help steer the model. With the right feedback mechanisms and guardrails, you can even use it for code generation! Hopefully I’ll have a few apps and tools out soon using AFM. I think embedded AI is the future, and in the next few years more platforms will come around to AI as a local API call, not an authorized HTTP request. That said, AFM is still incredibly premature and I’m experimenting with newer models that perform much better.
I’m also working on agents in Swift with the AFM, just having it locally already installed is a huge selling point. I think narrowly-focused agents with good tooling and architecture could accomplish quite a bit, with tradeoffs in speed and cost. But I’m under the assumption that local models (like frontier models) will only get better with time
Love the idea, would be great to see a modes to switch to circular interface for the Time Round 2. Definitely lots of potential for rapid iteration on new watch faces and apps!
I tried Elixir a few months back with several different models (GPT, Claude, and Gemini). I’m not an Elixir or BEAM developer, but the results were quite poor. I rarely got it to generate syntactically correct Elixir (let alone idiomatic). It often hallucinated standard library functions that didn’t exist. Since I had very little prior experience, steering the models didn’t go well. I’ve since been using them for JS/ TS, Kotlin/ Java, and a few other tasks where I’m much more familiar.
My takeaway was that these models excel at popular languages where there’s ample training material, but struggle where the languages change rapidly or are relatively “niche.” I’m sure they’ve since gotten better, so perhaps my perception is already out of date.
Cool app! It’s quite polished and scrolling is smooth on a 13 Mini. Some quick thoughts:
- I didn’t see any loading indicator, and some videos took multiple seconds to load
- When I scroll up then back down, I get a white gradient at the bottom of the screen that goes away after a few seconds
- I’d like to be able to expand the description text, but clicking it didn’t do anything
- Without signing in, my initial feed was almost entirely ICE videos
I run a podcast app for flip phones called PodLP. Revenue hasn’t always been $500/month, but averaged over the past 5 years it’s been over $500. It’s been primarily from sponsorships (podcasts get featured on the homepage to get more listeners), although next year looks more uncertain so I’m looking at other avenues.
As a counterpoint, consider reading Kevin Kelly’s “The Inevitable.” I also avoid most social media and have an aversion to all things “smart,” but these may actually be “inevitable.” In a capitalist society, these forces feel inevitable because there’s no Anubis balancing the scales. Only local decisions, often short-sighted. When demand is being created in a market, people will compete to capture it any way possible. Some of those ideas will last, and others will be a flash in the pan. It’s not clear to me that if you reran the tapes of history again, you’d get a significantly different outcome that didn’t include things like short-form video to exploit attention.