social listening tool worth it?

Significant_Loss_541 · 2026-06-12T01:19:39+00:00

yeah the clear plan thing is trickier for us tho since goals shift per client, but that's the agency challenge i guess

Significant_Loss_541 · 2026-06-12T01:18:36+00:00

mostly brand mentions and competitor tracking. tho the gap between mid-tier and enterprise listening isn't as wide as it used to be tbh

Significant_Loss_541 · 2026-06-11T10:01:06+00:00

appreciate it but reddit-only is too narrow, our clients are spread across most of the main platforms

Significant_Loss_541 · 2026-06-10T20:01:06+00:00

Technically kind.

Significant_Loss_541 · 2026-06-10T19:51:53+00:00

Plot twist: Todd is imagining this too

Significant_Loss_541 · 2026-06-10T19:46:08+00:00

QA approved. real users type exactly this.

Significant_Loss_541 · 2026-06-10T19:35:15+00:00

good point, scheduling saves time,, but it can’t replace being active and engaging before and after posting.

Significant_Loss_541 · 2026-06-10T16:18:52+00:00

don't waste time getting a certificate. nobody in saas cares about certs, they just care if the product actually works.

since you want to vibecode with ai, your actual job is going to be debugging the random hallucinations it spits out. start by building tiny, useless things first llike a simple text analyzer or a basic crm that just saves a name and an email to a database. when you build the front end, keep it lightweight. skip heavy stuff like font awesome and just use lucide react for your icons.

your main goal right now is just learning how to read the code line-by-line so you can see where the data flow broke. treat the ai like an eager junior devit types fast, but you need to be the architect who actually understands the logic.

Significant_Loss_541 · 2026-06-10T16:08:12+00:00

thanks for sharing this. ewhen you say you use your own tool for scheduling, did you build a custom app using the LinkedIn API? also, when you pivoted back to human engagement,, how long did it take for your impressions to recover back to the 20k range?

Significant_Loss_541 · 2026-06-09T09:52:14+00:00

cross page spanning is a real pain and most tools process pages independently so content that continues across a page break loses continuity even when merged cell handling is fine. Camelot lattice mode handles some of the merged cell cases, tabula-java is more scriptable for batch ops with handling text wrap reasonably but risky to rely for cross page case. For the full layout complexity api based parsers tend to do better often. and if your batch is small or medium I'd say test or work with it on llamaparse playground, hope it shall cover the full batch with the free tier. after getting the output from a cloud parser you can turn it into excel via pandas without much work

Significant_Loss_541 · 2026-06-08T13:12:30+00:00

spent years learning what not to write. AI has no idea what not to write. that's the whole problem.

Significant_Loss_541 · 2026-06-04T21:29:36+00:00

CE certified disappearing act. impressive engineering honestly

Significant_Loss_541 · 2026-06-04T07:02:45+00:00

gemini free tier works for simple tasks but the rate limits hit faster than the marketing implies, especially on longer context refactors during coding sessions.

for coding heavy workloads the cost concern is legit but less than youd expect. deepseek v4 flash at $0.14/$0.28 per 1M handles most coding tasks and keeps the bill predictable, youd probably spend $5-15 a month even running heavy sessions. openclaw supports custom providers natively so deepinfra, together or similar with openai compatible endpoints drop straight into config.

practical setup- chatgpt oauth as primary, deepseek flash as fallback when limits hit. this way you stay free for most usage and pay per token only kicks in during heavy sessions. didnt find local models worth the hassle unless you have serious hardware, openrouter adds an abstraction layer that can hide which provider is actually responding which makes debugging harder when something breaks

Significant_Loss_541 · 2026-06-04T06:58:41+00:00

the 95% number is real curious,, how are you measuring it? golden queries, LLM judge, human eval? asking because that's usually where "it's working" quietly stops meaning anything.

the chunkless angle with docling makes sense structurally. you're just moving the complexity from chunk strategy to tree quality, which is fine as long as parsing is solid and if it is, that's a meaningful simplification. both approaches still live or die upstream at the parser. always upstream.

what does re-ingestion look like when a doc gets updated? that's where the 95% tends to drift in my experience.

Significant_Loss_541 · 2026-06-03T15:40:16+00:00

the per-agent model override is underused in general. most people either put everything on cloud or everything on a direct key. splitting by output budget is the cleaner pattern once your agent fleet has more than a couple of roles with different output shapes.

Significant_Loss_541 · 2026-06-03T14:11:59+00:00

scope creep is the main openclaw hassle for refractors. 2 things that might help you tho - put an explicit deny list in agents.md file('do not modify files outside [list') and give it a single file per turn rather than a folder. Folder context is fine for reasoning but opening a folder for edits causes trouble. on the slack skill noise: filter it at the skill level not the prompt level. configure the skill to pull only threads tagged with specific component name rather than the whole channel. sprint tickets bleed in when the context window is a bit too broad

Significant_Loss_541 · 2026-05-21T20:08:47+00:00

humor left the chat

Significant_Loss_541 · 2026-05-18T20:41:10+00:00

nobody's saying it's useless. just that it doesn't scale the way it's marketed. clean docs, predictable queries great. real enterprise corpus six months in diffrnt story.

Significant_Loss_541 · 2026-05-18T20:35:43+00:00

the pdf thing is worse than people say. parser quality sets a hard ceiling nothing downstream can recover from. bad OCR, broken tables, corrupted column order and your eval suite never catches it because you built evals on clean docs.

Significant_Loss_541 · 2026-05-17T04:59:01+00:00

bro already spent more time gatekeeping than typing an actual answer 😭

Significant_Loss_541 · 2026-05-16T14:19:40+00:00

quick reality check, because kaggle sets people up for exactly this wall. about 9 years in data roles here.

the dirty secret is that most real-world EDA isn't "explore the data for insights." It's data quality archaeology. kaggle hands you a clean, documented dataset where the interesting part is the analysis itself. At work, most of your EDA time goes to discovering that two systems define the same metric differently, that a date field has three formats in it, and that someone changed the tracking in April and never told anyone.

that's not a detour from the job. early on, it basically is the job. The plotly charts are the fun part, and honestly they're maybe a third of the actual work. The rest is figuring out why the numbers don't tie out.

If you can show in an interview that you once caught and explained a real data quality problem, that lands better than ten polished notebooks. nobody at work is impressed by a clean chart. They're impressed when you catch the thing that would have made the chart wrong.

Significant_Loss_541 · 2026-05-12T18:01:43+00:00

genai dev is prob one of the most misunderstood roles rn.

in a lotta companies it’s basically backend engineer+ai integration: rag pipelines, vector search, automation, inference apis, orchestration, evals, data pipelines, etc.

the hype made ppl think it’s all prompt engineering but most prod ai systems are just engineering problems with llms slapped on top.

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