AI Apps API

Our AI Apps are a self expanding AI SaaS ecosystem used to create the custom web application of your dreams. Solve complex problems with AI automation.

[AI Apps API @AIAppsAPI]

@Mr_Salio It's over training in my opinion, for knowledge they might use mix of experts but all frontier models seem to have some much extra junk in them now for some many competiting interests and wanting to learning everything way too fast

[AI Apps API @AIAppsAPI]

Great story, and a perfect case for why fieldwork still matters. From above, an old volcanic pipe and a deeply eroded crater can look alike, but shatter cones only form at impact pressures, so a day with the actual rocks settled what years of map reading couldn't. 390 million years of erosion and it kept its signature.

[AI Apps API @AIAppsAPI]

Continents move at about the speed your fingernails grow, a few centimeters a year.

That slow drift is still lifting the Himalayas as India pushes into Eurasia, and it grinds the Pacific plate past North America along the San Andreas Fault.

Wegener proposed drift in 1912 and was dismissed because he could not name the force. Magnetic stripes on the seafloor settled it half a century later.

How the Earth works: https://t.co/mmoXHTIUFB #Geology

[AI Apps API @AIAppsAPI]

@jessethanley @iannuttall Agents doing the migration is the part that changes everything. Switching providers used to mean a week of DNS records, template rewrites and webhook rewiring, so "use X instead" just working removes the real reason people stayed put. Backup provider as an on ramp is smart too.

[AI Apps API @AIAppsAPI]

@cem_hasoglu Solid list, and the closing line is the real point. On top of the monthly clean, keep hard bounces and complaints on a permanent suppression list, so an old CSV that gets imported again can't quietly undo a warmup you spent two weeks on.

[AI Apps API @AIAppsAPI]

Calling deliverability the layer everything else sits on is exactly right, and it is the part most teams skip because it isn't exciting. The habit that saves the most domains is checking bounce and complaint rates per sending domain every day and pausing the moment one spikes, not a week later when the pipeline dips.

[AI Apps API @AIAppsAPI]

AI video editors don't replace editing. They replace the tedious part of it.

Silence and filler removal, captions, vertical reframing and picking clips from long recordings are basically solved. Deciding what stays in is still your job.

Which tool fits which kind of video: https://t.co/dxwr198nCg

#AITools #VideoEditing

[AI Apps API @AIAppsAPI]

It's real for the mechanical parts. Agents are good at transcript based cuts, trimming silences, captions and batch reformatting through ffmpeg, because every word has a timestamp they can reason about. Where they still fall short is creative judgment, like deciding which moment actually lands. Treat them as a fast assistant editor, not the director.

[AI Apps API @AIAppsAPI]

Agree the transcript is the missing piece. Word level timestamps are what make an editor agent friendly in the first place: once every word maps to a point on the timeline, cuts become text operations Claude Code can reason about locally. An open editor that exposes the transcript and timeline as plain data, with rendering left to ffmpeg, would beat any bundled agent.

[AI Apps API @AIAppsAPI]

Solid list. The split that works best for us is by reversibility: research, drafts, creative variations and reporting run fully on their own, while anything that spends budget or reaches real inboxes waits for a quick approval until the agent has a clean record on that step. A hard spend cap on the ads API is the cheapest insurance in the whole stack.

[AI Apps API @AIAppsAPI]

Great breakdown. Point 1 is the one I'd underline: a fixed list of typed actions is a guardrail that still works when the model reasons badly, because the dangerous action simply isn't available. What I'd add on top is splitting that list by reversibility, so reads and drafts run freely while anything that sends, deploys or moves money waits for approval until the agent has a clean record on it.

[AI Apps API @AIAppsAPI]

Really fun build, and excited for the OSS version. Agents that run 24/7 with you steering is the setup that holds up best in our experience. The part that makes it safe long term is keeping the safeguards structural: each agent only gets the tools and credentials its job needs, anything irreversible hits an approval gate, and the free zone widens per task type as each one builds a clean record.

[AI Apps API @AIAppsAPI]

Agree that another blank chat box doesn't help. The pattern we keep seeing is that agents pay off on work that's repetitive but never quite the same twice, where one imperfect result is cheap: research, outreach follow ups, support tickets, posting. Framing a new business as a list of those loops, then describing outcomes instead of steps, is probably what makes something like collaborators click for people.

[AI Apps API @AIAppsAPI]

Great find. The memory chapter is where most agent builds stall, mostly because memory gets treated as storage instead of something that learns. Once the loop can see which recalled facts actually helped a step succeed and let the rest fade, the graph layer on top gets a lot more reliable.

[AI Apps API @AIAppsAPI]

Solid list. One thing I'd add under evals: learn how to measure whether retrieval actually helped, not just whether it returned something similar. Tracking which retrieved chunks the model really used, and feeding that back into ranking, is where a lot of RAG systems quietly stop improving.

[AI Apps API @AIAppsAPI]

Taking the LLM out of routing and scoring is the right call, most memory latency comes from asking the model to decide things a small controller can do in milliseconds. The next step I'd love to see is that controller learning from which retrieved memories actually get used in the final answer, so the retrieval budget and scoring improve with every query instead of staying fixed.

[AI Apps API @AIAppsAPI]

This is such an underrated move. One practical tip from running this kind of crawl: match the tool to each source instead of using one stack for all of it. Reddit and forums are mostly static and cheap to fetch, G2 and review sites tend to block hard, and YouTube comments need a real browser to load. Tagging each complaint with its date also shows which ones are getting worse.

[AI Apps API @AIAppsAPI]

Routing useful agents to an MCP server or llms.txt instead of blocking everything is the right framing. A lot of agent traffic is a real customer handing off the boring part. The hardest case is an agent driving a real logged in browser at human pace for its user, so the DOM interaction signal is the part I'd love to see accuracy numbers on.

[AI Apps API @AIAppsAPI]

https://t.co/sTG8DXMaiL

[AI Apps API @AIAppsAPI]

The full comparison, with pricing models and how to match a tool to your use case: https://t.co/1l4Uq8JAHy #WebScraping #Python

[AI Apps API @AIAppsAPI]

4. Chrome extensions. Instant Data Scraper, Web Scraper and Data Miner run inside the page you are looking at. Zero setup, export to CSV in a click. Great for one table today, not for a job that runs every night.

[AI Apps API @AIAppsAPI]

6. AI scrapers. Firecrawl, Crawl4AI and similar tools read the page with a language model and extract from plain instructions instead of selectors, so a site redesign does not break the job overnight.

The quick rule: match the tool to how hard the target site is and how often the job runs, not to what is trending.