68 karma · joined September 12, 2026
2. For most people bandwidth did not increase at the same rate as video file sizes during that period, streaming could do smart things like serve transcoded media sized to fit your screen and internet connection. It's only recently with HEVC and AV1 that video file sizes have caught up.
3. Streaming can be easily monetized, p2p generally cannot.
There are a minority of users who care about being able to host videos that violate YouTube/TikTok/Instagram policies. Unfortunately, a significant part of that minority is working with infringing or illegal content, which both prevents ad networks from working with you and attracts attention from law enforcement.
Point of clarification - GPS/GNSS does not work that way. The GPS in your phone only _listens_ to GPS signals and is not transmitting to/observable by the satellite. https://ciechanow.ski/gps/
Temporal is fully open source, you can run the entire thing on your laptop or your own server. People pay for the SaaS anyway, because dealing with the distributed computing problems is hard.
(There are some other projects inspired by Temporal as well, but if you want to run something in-house, you're probably funding a minimum of 2-3 engineers to handle on-call, and for that price the SaaS is competitive).
In Temporal, you use their SDK to mark which code is either:
- Deterministic without external dependencies on network, disk, clock, etc.
- Non-deterministic (e.g. accesses a filesystem, dependent on clock time, talks over the network)
You can write the code without handling flakiness or retries and the Temporal control plane handles all the progress tracking and retries for you. Progress is tracked at the individual line of code for deterministic code, or for non-deterministic code, tracked at function boundaries defined by the programmer. You buy into more complexity upfront, but it makes the application code way simpler and easier to manage overall.
They do a lot of other cool stuff on top of all this, and their Temporal Worker Controller architecture is particularly well suited to running massive scale processing/AI workloads on Kubernetes (handles a lot of stuff like autoscaling without interrupting work, rainbow version rollout, etc.)
It's pretty simple, you could probably set up something like that by:
Configure some kind of CLI tool to talk to your ticketing system and git repo so you can programmatically interact with them. If you don't have a ticketing system, instruct the agent to use local text or markdown files to track issues and progress.
Ideally, make your code runnable in a way the agent can use. For my webapps I build a test harness so that I can run all the endpoints and workflows via reproducible tests against an embedded database. This is easier than it sounds, e.g. there are libraries out there to embed PostgreSQL or SQLite into source code, you can set up a test harness so you can run unit tests, integration tests and workflow tests that use your real frontend, server and database.
Paste this comment thread into the agent prompt and tell it to run a similar loop on your code base: a session that searches for vulns and writes up a report, some way for a human to do a review pass on the report, a session that indexes the reviewed findings into tickets, and sessions that fix the fixable issues and submit patches to your repo. The next search session should first read all the open issues so it doesn't duplicate work of earlier sessions.
LESS IS MORE - avoid fancy agent tooling and skills, don't cargo cult from others, build your own tools as you find your own needs. If something can be automated, use the agent to write tools and tests for it, don't just keep prodding the agent to do it.
This isn't a joke, this is now part of my pre-launch SOP. I even have it tracking everything so I can log stuff to fix vs. known shippables vs intentional design/false positives vs. upstream stuff which doesn't have a fix available yet, and keep track of which builds have the fixes. Almost entirely automated, I mostly review the findings and do some categorization/enrichment during the pentest review stage, and do a human code review pass as patches are submitted.
Stuff that used to take me multiple hours to write a fix for and then weeks to get code reviewed and deployed now get done in minutes.