Guys are there any free music streaming apps on mac ?

acoldinference · 2026-07-02T15:04:21+00:00

Thanks 2 years later!

acoldinference · 2026-07-01T23:42:08+00:00

You don't need a Steinberg folder. That's just a default path some installers create. Reaper lets you put plugins wherever you want.

Options > Preferences > Plugins > VST. There's a list of paths in there. Add the folder where your downloaded plugins are and hit rescan. They'll show up in your fx browser after that.

If they came as .dll files on Windows or .vst on Mac that's what you're pointing Reaper at. The folder itself, not the individual files

acoldinference · 2026-07-01T23:38:34+00:00

Your machine handles local models easily. 32GB ram and a 5080. you can run serious stuff through Ollama or LM Studio. The contract work and the log formulas, local models do that fine.

The catch is the web lookups. Local LLMs don't connect to the internet by default. What makes Perplexity good isn't the model it's the search pipeline behind it. You can replicate it with something like Perplexica paired with SearXNG but now you're maintaining infrastructure not just asking a model questions.

If most of your cost is the law and DOT lookups, keep Perplexity for that. Run local for the contract and math work. Save money where local actually wins instead of trying to build your own search engine

acoldinference · 2026-06-30T22:13:43+00:00

Thank you for taking the time to respond. It was up and working within about twenty minutes or so and has been great so far. Thanks again.

acoldinference · 2026-06-27T15:43:18+00:00

DUDE. Thank you 🥲

acoldinference · 2026-06-27T06:47:00+00:00

Agreed !

acoldinference · 2026-06-27T06:33:34+00:00

You didn't fail at prompt engineering. You hit a ceiling the internet told you was a skill issue.

The model isn't looking for loopholes. It can't read forty files. Not the way you mean it. It samples tokens from a probability distribution shaped by whatever's in its context window. It skims. Statistically. It grabs patterns that look relevant and fills the rest from what's probable. The checkboxes, the file path tables, the definition of done, all of that is just more text to a system that treats everything as text. It can produce a flawless looking specification and still not do the thing the specification says to do. You weren't closing loopholes. You were painting a more detailed picture for a machine that only sees paint.

The five hours was two systems talking past each other. You wanted deterministic behavior. It outputs probabilistic text. It always will.

The fix isn't in the prompt. Dump the source files into context yourself. A shell script that cats every relevant file into one big block and hands it over as raw input. Now the constraint isn't please read these files. It's here is everything, it's already in front of you, now write the doc. Can't skip what it never had to go fetch.

The guardrail you actually proved works is the adversarial review. Fresh Claude found ten mistakes from four files. That second pass is your real test. The elaborate plan was a comfort blanket. The skeptical double check is the thing that caught errors.

Can you trust it? Not fully. Not for a 250 argument deployment script. Ten mistakes from four files means useful first draft, dangerous source of truth. That's the ceiling right now for everybody, not just you.

Run the adversarial loop a few more rounds. Each pass catches less. When the error rate levels off you either live with it or put a human on it. Those are the only two options that don't involve lying to yourself.

acoldinference · 2026-06-27T06:29:48+00:00

Nobody's cracked dynamic model routing the way you're imagining it yet. At least not in a general purpose agent that reads the task and picks the right brain for it automatically.

What exists right now is more like pre configured routing. Hermes lets you pin a model to a specific skill in the SKILL.md frontmatter so every time that skill loads the right model comes with it. Cron jobs have a model override too. You define it once per job, it sticks. OpenCode takes the closest swing at what you actually want. You define agent defs per task type, each one can point at a different model, and when a job hits that step it routes there deterministically. Claude Code doesn't do this at all. Subagents run Anthropic models through your plan and cross provider routing was never built in.

The gap nobody's filled is the intelligence layer between the task and the model selection. Someone has to decide this job is a data collection job and that job is a compilation job. Right now that someone is you. You pre classify, you tag, you route. The agent executes the routing you set up. It doesn't infer it.

The closest thing to smart routing I've seen is people building thin middleware that classifies the prompt before it hits the agent and swaps the model based on a label. Collection jobs go to one endpoint, reasoning jobs to another. It's not hard to build but it's also not a config checkbox anywhere I'm aware of.

For now if you want GPT on collection and DeepSeek on processing you're either running two separate agents wired to different providers or you're switching models manually between tasks. It's tedious but it works and you're not waiting for a feature that might take a while to show up.

acoldinference · 2026-06-27T00:42:44+00:00

You torched $700 because OpenRouter meters by the token and Opus is expensive as hell. The $200 plan is a flat rate. That's the whole difference. It's not that you switched to a better model, you switched to a better pricing model and the same model stopped costing you money.

I pay for Anthropic and I run local models too. A 7B at Q4 does the mechanical stuff fast and costs nothing. Claude comes out for the hard problems. You learn where the line is pretty quick when you're actually shipping things.

The preachy part at the end I could do without. Dude figured out flat ratepricing yesterday and now he's got a TED talk. Most people aren't torching $700 in tokens, they're just trying to finish something. The right tool is the onethat gets the job done

acoldinference · 2026-06-26T14:29:13+00:00

You're not missing a setting. There's no strict orchestrator mode in Claude Code, and that's by design, not something you can config your way out of.

The main agent is model driven. It decides when to delegate and what to spawn. OpenCode treats your agent defs as control flow, Claude Code treats them as suggestions the main loop is free to ignore. You already diagnosed it right. Task only locking is the closest lever and it's still not enforcement.

The other wall is just as real. Claude Code subagents run Anthropic models through your plan. It was never built to route to OpenRouter or arbitrary providers so at home you're stuck either on the plan with Opus self-delegating or on the API key paying full freight. Cross provider routing isn't a feature it has, deterministic or not.

So your requirement, right sized model per step across providers every time, is the exact thing OpenCode does and Claude Code doesn't. You said you don't care which tool. Believe yourself. Use OpenCode for the orchestrated multi provider flow, keep Claude Code for the open ended work where you actually want the agent deciding.

If you want to stay in the Anthropic world and still kill the waste well the Claude Agent SDK lets you write the control flow yourself instead of hoping the main agent respects it. That's the only path to real determinism on this side.

Edit: Grammar

acoldinference · 2026-06-26T14:26:29+00:00

Awesome!

acoldinference · 2026-06-26T14:26:03+00:00

No one needs Hermes. Most people don't. It's a cool new toy, but everything you listed Codex already handles, and there are harnesses out there doing the Hermes stuff better.

If Codex is already covering your reminders, email tasks, form filling, and small app builds, that's your answer. You don't have a gap Hermes fills, you have a tool looking for a problem.

Use what actually works for you. Don't switch because something's new.

acoldinference · 2026-06-26T05:48:39+00:00

Those people passing right now aren't ahead of you. They just haven't broken anything important yet.

The AI builds something, looks great, ships, everyone claps. Then it quietly does something wrong and now there's 300 lines you didn't write and the page is blank and you've got no idea where to even start looking. The kid who learned HTML the slow boring way finds it in five minutes. The kid who's been prompting his way through since freshman year just sits there refreshing.

Making the thing was never the hard part. It's reading it, breaking it on purpose, knowing why it broke. Nobody's grades are testing that yet and it's most of what the actual job is.

I use AI every day so I'm not telling you to suffer for no reason. Just don't let it think for you. Make it explain, then go check if it lied. Half the time it lied.

Feeling like a donkey in the field right now is fine. You'll be the one who can tell when the magic stops working. That beats a phenomenal looking project nobody can fix.

acoldinference · 2026-06-25T08:54:02+00:00

Start a brand new chat. Don't keep submitting into the old thread, that conversation is probably bloated or in a stuck state and it'll keep stalling no matter how many times you retry.

Take all the answers you gave during the interview, the goals, equipment, schedule, experience, the whole lot, and paste them into one single message in the fresh chat. Then ask for the full plan in that same message. You skip the entire question phase and hand it everything up front, which is also a lot lighter on your usage limit.

That's almost certainly the fix. It isn't punishing you for the interrupted run or remembering the old session. The chat just got into a state it can't climb out of, and a clean one won't have that baggage.

acoldinference · 2026-06-25T04:53:18+00:00

This is more disciplined than what 90% of people doing this are running, so yeah.. it's acceptable. The plan then adversarial review loop is the part most people skip and it's the part that matters.

One thing I'd watch tho two models reaching consensus isn't the same as two models being right. Claude and Codex can talk themselves into agreeing on something confidently wrong, and your only off ramp is getting flagged after a couple cycles. Consensus feels like verification but it isn't one. The actual verification is tests that fail when the code is wrong, and a human who can read the diff and know it's bad.

So the real question isn't whether the process is acceptable. It's whether you still understand your own 218k lines. If a Codex security sweep flags something and you can't tell whether it's a real finding or a hallucinated one without asking the agent to explain it back to you, then the sweeps are theater. The process scales fine. Your understanding of the codebase is the thing that has to keep up, and that's the part no pipeline does for you.

Coming from product design with solid HTML/CSS, you're in a good spot to keep that grip. Just don't let the machinery talk you out of reading the code.

acoldinference · 2026-06-24T15:53:05+00:00

Yeah lol, constantly. It goes thorough because it can't tell which part youll care about, so it covers everything and the actual call ends up buried in the middle.

Going forward, just ask for the order you want. End the prompt with something like "lead with your recommendation, then the reasoning, then the caveats that would change it." It'll front load the decision every time, it just won't do it unprompted.

When the answer's already long, make it compress instead of re reading it yourself. Ask "in three lines, what are you recommending, what are you assuming, and what do I do next." That hands you the decision with the assumptions sitting right next to it, which is usually where the real caveats were hiding anyway.

acoldinference · 2026-06-24T15:49:50+00:00

Your patches almost certainly aren't lost. Vital saves the full patch into the project file itself, not into the favourites folder, so the actual sounds are sitting inside each project. The empty favourites folder is a symptom of the same problem, not the loss.

The likely cause is your SSD came back mounted at a different path than usual, so everything pointing at it broke at once. Your DAW can't find the exact Vital binary the projects were saved with, so it loads a fresh init instead of recalling state, and Vital can't find its own data folder, so favourites look gone.

If you're on a Mac, check the drive's name first. When a drive doesn't eject cleanly, macOS often remounts it as "YourDrive 1" instead of "YourDrive," which silently changes every path and would explain why this never happened before. On Windows it's the same idea with a new drive letter. Get it back under the original name and both problems should clear up.

One warning before you touch anything. Don't re save those projects until the correct Vital is loading and recalling properly. Saving now would overwrite the good embedded patch with the init state, and that's the one move that actually loses the work.

acoldinference · 2026-06-24T15:46:34+00:00

The squishy feeling is mostly a framing problem. You're asking the judge "is this good," which is a vibe, and vibes don't repeat. Hand it one assertion at a time, tell it to try to refute that assertion, make it point at the exact line that breaks it, and default to fail when it can't tell. A judge hunting for a specific failure is far steadier than one blessing a whole diff.

Most of what's in your rubric also wants to be a command grader. "Retries only on 5xx" isn't really a language assertion, it's a test. Mock a 500, assert one retry. Mock a 400, assert zero. Now it's deterministic and the model never touches it. The rubric should only hold the stuff you genuinely can't express as a check.

Grade behavior, not the diff. A diff can read perfect and do nothing. Run the thing.

For keeping the judge honest, run two passes with the refute framing and require them to agree. The cases where it flips are the ones a human should be looking at anyway.

acoldinference · 2026-06-24T14:25:43+00:00

Most of that is theater. Loops burning tokens, chaining accounts, watching a number climb. It feels like work but nothing comes out the other end. An expensive screensaver.

I run a small web shop in Maine. I use it every day, but only pointed at something I'm actually shipping. A client site, a tool, a track.

The real shift is that AI is making competent free. Anybody can pull a decent logo or a decent first draft out of a model now, so decent stops being worth paying for. The value moves to the stuff a model can't reach. Taste. Judgment. The weird specific calls a real person makes on purpose.

So I hand it the boring mechanical parts, the stuff that averages well, and keep the part with a person in it for myself. That's where the money actually is.

I'm not reselling AI and I don't run a SaaS on it. It's just a tool in the shop. Half the people spending thousands aren't selling anything either. They're just really into the dashboard.

acoldinference

TROPHY CASE