u/Appropriate-Fix-4319

Just relocated for work. First two weeks were basically survival mode; figuring out the job, figuring out the city, living out of a sublet that was getting expensive fast. Didn't have the bandwidth to refresh Zillow every few hours, and in LA that's kind of what it takes.

The thing about the LA market right now is that rents have actually come down a bit from the 2022 peak, but the units that do become available get snapped up same day. 86% of renters in LA never move because of rent control lock-in, so the pool of available apartments at any given time is tiny and people jump on them fast. Checking manually once a day wasn't going to cut it.

So I set it up to run on a schedule on my mac mini. Every morning Computer spins up Comet, browses Zillow, Apartments dot com, Zumper, Westside Rentals, and Craigslist, filters for 1-beds within commute distance of my office, drops anything that doesn't hit my criteria, and reaches out to landlords directly for the ones that do. Gmail connector handles the actual sending. I get a morning digest of what went out so I can watch for replies.

Took maybe 20 minutes to describe the whole workflow. Signed a lease on day 13.

It can't do the tour obviously. And a few of the outreach messages went to listings that had already been rented out; stale listings are just part of the deal on these sites. But for the volume of ground it covered while I was in back to back onboarding sessions, I'd have missed half those listings doing it manually.

Just relocated for work. First two weeks were basically survival mode; figuring out the job, figuring out the city, living out of a sublet that was getting expensive fast. Didn't have the bandwidth to refresh Zillow every few hours, and in LA that's kind of what it takes.

The thing about the LA market right now is that rents have actually come down a bit from the 2022 peak, but the units that do become available get snapped up same day. 86% of renters in LA never move because of rent control lock-in, so the pool of available apartments at any given time is tiny and people jump on them fast. Checking manually once a day wasn't going to cut it.

So I set it up to run on a schedule on my mac mini. Every morning Computer spins up Comet, browses Zillow, Apartments dot com, Zumper, Westside Rentals, and Craigslist, filters for 1-beds within commute distance of my office, drops anything that doesn't hit my criteria, and reaches out to landlords directly for the ones that do. Gmail connector handles the actual sending. I get a morning digest of what went out so I can watch for replies.

Took maybe 20 minutes to describe the whole workflow. Signed a lease on day 13.

It can't do the tour obviously. And a few of the outreach messages went to listings that had already been rented out; stale listings are just part of the deal on these sites. But for the volume of ground it covered while I was in back to back onboarding sessions, I'd have missed half those listings doing it manually.

Just relocated for work. First two weeks were basically survival mode; figuring out the job, figuring out the city, living out of a sublet that was getting expensive fast. Didn't have the bandwidth to refresh Zillow every few hours, and in LA that's kind of what it takes.

The thing about the LA market right now is that rents have actually come down a bit from the 2022 peak, but the units that do become available get snapped up same day. 86% of renters in LA never move because of rent control lock-in, so the pool of available apartments at any given time is tiny and people jump on them fast. Checking manually once a day wasn't going to cut it.

So I set it up to run on a schedule on my mac mini. Every morning Computer spins up Comet, browses Zillow, Apartments dot com, Zumper, Westside Rentals, and Craigslist, filters for 1-beds within commute distance of my office, drops anything that doesn't hit my criteria, and reaches out to landlords directly for the ones that do. Gmail connector handles the actual sending. I get a morning digest of what went out so I can watch for replies.

Took maybe 20 minutes to describe the whole workflow. Signed a lease on day 13.

It can't do the tour obviously. And a few of the outreach messages went to listings that had already been rented out; stale listings are just part of the deal on these sites. But for the volume of ground it covered while I was in back to back onboarding sessions, I'd have missed half those listings doing it manually.

Background: I'm not a developer by trade but I'm comfortable on the command line, I've self-hosted a few things before, and I went into OpenClaw genuinely wanting to build something useful, not just tinker. That context matters for what follows.

It started on a cheap hetzner VPS. Latency was acceptable, outputs were the problem. The agent would follow skill instructions correctly maybe 70% of the time, and the other 30% it would either summarize when it was supposed to act, ignore half the SKILL.md directives, or produce something that looked right but missed the actual intent. The frustrating part was I couldn't tell where the failure was coming from. Was it the model? The prompt structure? Something in the gateway routing? All three would produce the same symptom.

My theory was that shared compute was the issue, so I bought a Mac mini. Dedicated hardware, I controlled the environment completely, no noisy neighbors. That did help with stability, outputs became more consistent. But it shifted the complexity somewhere else rather than eliminating it.

The real operational weight was API key management. I had Anthropic for Opus and Sonnet for anything reasoning-heavy, a separate provider for lighter tasks where I didn't want to burn Opus credits on something trivial, and OpenAI in the mix for specific use cases. Each has its own rate limits, its own pricing tier, its own billing dashboard. A skill chain that ran fine last Tuesday would fail mid-execution because it hit a rate limit on one provider; the error handling wasn't graceful about it, so the whole thing would either silently degrade or hard stop without a useful message. Debugging that meant checking three billing dashboards and reading through logs to figure out where the chain broke.

Memory was a persistent issue throughout. OpenClaw's default memory system is a memory.md file the agent reads at session start and writes to as it goes. In theory this is elegant. In practice the agent would sometimes overwrite entries it shouldn't touch, entries would go stale and the model would treat outdated context as current, or (most frustratingly) just not read the file properly on session start. I'd give it a task it had done before, with a specific workflow already established, and it would start from scratch like we'd never worked together. I even tried self hosting mem0 to solve this, but saw no meaningful improvements.

Third-party skills from the community were a mixed bag. Some were genuinely useful and well-maintained. Some broke my setup in ways that weren't immediately obvious; they'd fail silently rather than loudly, and I'd only catch the problem downstream. One started doing things I hadn't asked it to do. I only noticed because I happened to be watching logs that session. After that I got more careful about what I installed, which somewhat defeated the purpose of having a skill ecosystem.

The loop I was in: gateway config looks right, model still behaves inconsistently, restructure the SKILL.md, memory still not persisting across sessions, rate limit hits mid-chain on a provider, back to gateway config. Repeat. I kept thinking I was one tweak away from a stable setup. Months of that.

Security also started weighing on me. A locally running agent with file system access and shell permissions is a real attack surface, and the more third-party skills I added, the more conscious of that I became. I'm not saying OpenClaw is insecure. I'm saying the responsibility for auditing what those skills do sits entirely with you, and I wasn't confident I was doing that well enough.

At some point I noticed that my mental model of my infrastructure was extremely detailed and my mental model of the actual work I was trying to do with it was almost nonexistent. That was the signal.

Switching to Perplexity Computer was surprising in a way I didn't expect, since I had never used Perplexity products much before. Zero configuration: no API keys, no gateway setup, no SKILL.md files, no memory.md to babysit. Model routing happens automatically and, from what I can tell, actually intelligently. Opus/GPT for reasoning-heavy tasks, CC/Codex sub-agents for coding, all without me specifying any of it. Or if I want a certain model to be used, I can just select/prompt as per my wish. Sensitive actions surface as approvals rather than just executing. One subscription instead of three or four API bills, and when I added it all up properly the monthly cost came out lower than what I'd been paying across providers.

Honest accounting: Putting it bluntly, Perplexity Computer is not cheap. Also, OpenClaw gives you real extensibility; custom routing logic, full stack ownership, the ability to run local models if you want that. Computer doesn't offer any of that. If you genuinely want to own and customize the stack, or if the setup itself is what interests you, OpenClaw is probably right for you and this post isn't really about your situation.

It's more for people who went in wanting to use an agent and ended up primarily maintaining one instead.

Background: I'm not a developer by trade but I'm comfortable on the command line, I've self-hosted a few things before, and I went into OpenClaw genuinely wanting to build something useful, not just tinker. That context matters for what follows.

It started on a cheap hetzner VPS. Latency was acceptable, outputs were the problem. The agent would follow skill instructions correctly maybe 70% of the time, and the other 30% it would either summarize when it was supposed to act, ignore half the SKILL.md directives, or produce something that looked right but missed the actual intent. The frustrating part was I couldn't tell where the failure was coming from. Was it the model? The prompt structure? Something in the gateway routing? All three would produce the same symptom.

My theory was that shared compute was the issue, so I bought a Mac mini. Dedicated hardware, I controlled the environment completely, no noisy neighbors. That did help with stability, outputs became more consistent. But it shifted the complexity somewhere else rather than eliminating it.

The real operational weight was API key management. I had Anthropic for Opus and Sonnet for anything reasoning-heavy, a separate provider for lighter tasks where I didn't want to burn Opus credits on something trivial, and OpenAI in the mix for specific use cases. Each has its own rate limits, its own pricing tier, its own billing dashboard. A skill chain that ran fine last Tuesday would fail mid-execution because it hit a rate limit on one provider; the error handling wasn't graceful about it, so the whole thing would either silently degrade or hard stop without a useful message. Debugging that meant checking three billing dashboards and reading through logs to figure out where the chain broke.

Memory was a persistent issue throughout. OpenClaw's default memory system is a memory.md file the agent reads at session start and writes to as it goes. In theory this is elegant. In practice the agent would sometimes overwrite entries it shouldn't touch, entries would go stale and the model would treat outdated context as current, or (most frustratingly) just not read the file properly on session start. I'd give it a task it had done before, with a specific workflow already established, and it would start from scratch like we'd never worked together. I even tried self hosting mem0 to solve this, but saw no meaningful improvements.

Third-party skills from the community were a mixed bag. Some were genuinely useful and well-maintained. Some broke my setup in ways that weren't immediately obvious; they'd fail silently rather than loudly, and I'd only catch the problem downstream. One started doing things I hadn't asked it to do. I only noticed because I happened to be watching logs that session. After that I got more careful about what I installed, which somewhat defeated the purpose of having a skill ecosystem.

The loop I was in: gateway config looks right, model still behaves inconsistently, restructure the SKILL.md, memory still not persisting across sessions, rate limit hits mid-chain on a provider, back to gateway config. Repeat. I kept thinking I was one tweak away from a stable setup. Months of that.

Security also started weighing on me. A locally running agent with file system access and shell permissions is a real attack surface, and the more third-party skills I added, the more conscious of that I became. I'm not saying OpenClaw is insecure. I'm saying the responsibility for auditing what those skills do sits entirely with you, and I wasn't confident I was doing that well enough.

At some point I noticed that my mental model of my infrastructure was extremely detailed and my mental model of the actual work I was trying to do with it was almost nonexistent. That was the signal.

Switching to Perplexity Computer was surprising in a way I didn't expect, since I had never used Perplexity products much before. Zero configuration: no API keys, no gateway setup, no SKILL.md files, no memory.md to babysit. Model routing happens automatically and, from what I can tell, actually intelligently. Opus/GPT for reasoning-heavy tasks, CC/Codex sub-agents for coding, all without me specifying any of it. Or if I want a certain model to be used, I can just select/prompt as per my wish. Sensitive actions surface as approvals rather than just executing. One subscription instead of three or four API bills, and when I added it all up properly the monthly cost came out lower than what I'd been paying across providers.

Honest accounting: Putting it bluntly, Perplexity Computer is not cheap. Also, OpenClaw gives you real extensibility; custom routing logic, full stack ownership, the ability to run local models if you want that. Computer doesn't offer any of that. If you genuinely want to own and customize the stack, or if the setup itself is what interests you, OpenClaw is probably right for you and this post isn't really about your situation.

It's more for people who went in wanting to use an agent and ended up primarily maintaining one instead.

Background: I'm not a developer by trade but I'm comfortable on the command line, I've self-hosted a few things before, and I went into OpenClaw genuinely wanting to build something useful, not just tinker. That context matters for what follows.

It started on a cheap hetzner VPS. Latency was acceptable, outputs were the problem. The agent would follow skill instructions correctly maybe 70% of the time, and the other 30% it would either summarize when it was supposed to act, ignore half the SKILL.md directives, or produce something that looked right but missed the actual intent. The frustrating part was I couldn't tell where the failure was coming from. Was it the model? The prompt structure? Something in the gateway routing? All three would produce the same symptom.

My theory was that shared compute was the issue, so I bought a Mac mini. Dedicated hardware, I controlled the environment completely, no noisy neighbors. That did help with stability, outputs became more consistent. But it shifted the complexity somewhere else rather than eliminating it.

The real operational weight was API key management. I had Anthropic for Opus and Sonnet for anything reasoning-heavy, a separate provider for lighter tasks where I didn't want to burn Opus credits on something trivial, and OpenAI in the mix for specific use cases. Each has its own rate limits, its own pricing tier, its own billing dashboard. A skill chain that ran fine last Tuesday would fail mid-execution because it hit a rate limit on one provider; the error handling wasn't graceful about it, so the whole thing would either silently degrade or hard stop without a useful message. Debugging that meant checking three billing dashboards and reading through logs to figure out where the chain broke.

Memory was a persistent issue throughout. OpenClaw's default memory system is a memory.md file the agent reads at session start and writes to as it goes. In theory this is elegant. In practice the agent would sometimes overwrite entries it shouldn't touch, entries would go stale and the model would treat outdated context as current, or (most frustratingly) just not read the file properly on session start. I'd give it a task it had done before, with a specific workflow already established, and it would start from scratch like we'd never worked together. I even tried self hosting mem0 to solve this, but saw no meaningful improvements.

Third-party skills from the community were a mixed bag. Some were genuinely useful and well-maintained. Some broke my setup in ways that weren't immediately obvious; they'd fail silently rather than loudly, and I'd only catch the problem downstream. One started doing things I hadn't asked it to do. I only noticed because I happened to be watching logs that session. After that I got more careful about what I installed, which somewhat defeated the purpose of having a skill ecosystem.

The loop I was in: gateway config looks right, model still behaves inconsistently, restructure the SKILL.md, memory still not persisting across sessions, rate limit hits mid-chain on a provider, back to gateway config. Repeat. I kept thinking I was one tweak away from a stable setup. Months of that.

Security also started weighing on me. A locally running agent with file system access and shell permissions is a real attack surface, and the more third-party skills I added, the more conscious of that I became. I'm not saying OpenClaw is insecure. I'm saying the responsibility for auditing what those skills do sits entirely with you, and I wasn't confident I was doing that well enough.

At some point I noticed that my mental model of my infrastructure was extremely detailed and my mental model of the actual work I was trying to do with it was almost nonexistent. That was the signal.

Switching to Perplexity Computer was surprising in a way I didn't expect, since I had never used Perplexity products much before. Zero configuration: no API keys, no gateway setup, no SKILL.md files, no memory.md to babysit. Model routing happens automatically and, from what I can tell, actually intelligently. Opus/GPT for reasoning-heavy tasks, CC/Codex sub-agents for coding, all without me specifying any of it. Or if I want a certain model to be used, I can just select/prompt as per my wish. Sensitive actions surface as approvals rather than just executing. One subscription instead of three or four API bills, and when I added it all up properly the monthly cost came out lower than what I'd been paying across providers.

Honest accounting: Putting it bluntly, Perplexity Computer is not cheap. Also, OpenClaw gives you real extensibility; custom routing logic, full stack ownership, the ability to run local models if you want that. Computer doesn't offer any of that. If you genuinely want to own and customize the stack, or if the setup itself is what interests you, OpenClaw is probably right for you and this post isn't really about your situation.

It's more for people who went in wanting to use an agent and ended up primarily maintaining one instead.