there is obviously productivity increase with AI but it might have a ceiling. AI leader bluffing spending trillions is a joke. cost of ai gets cheaper, compute spend getting higher is just confusing. the gap is really huge. it seems just selling hype
we currently support 1000+ app integration. we have rolled out 117+ apps that simple to connect. We are rolling out the rest since they are not non-technical friendly.
I have been experimenting with openclaw for the past 3 months and I am Software Engineer and I had difficulties setting it up and managing it. I almost bought Mac Studio to run it, bought into the hype. I do believe it absolutely amazing product. I recently launched AlitaGPT.COM (Alita.com was already registered) to provide a managed openclaw with hundreds of custom application integration. I used GCP GKE -Autopilot which is dedicated node for every customer which is costly on average $30-45/customer + AI API COST. I have launched couple managed openclaw on AlitaGPT and tried OpenAI GPT 5.1, worst and annoying models, it keeps asking for confirmation 10 times before doing a simple task. MiniMAX and Claude models tend to be better AI models at doing agentic tasks.
If @SAM_ATMAN keeps at this rate, MiniMax and Claude will takeover for B2B and B2C market.
not promotional post, but I would love to hear insights of HN engineers on AlitaGPT.COM . I built it for non-technical users who have no idea what openclaw is and I have integrated hundred of app OAUTH2. adding more apps.
I have not done SOC 2 audit yet. LogClaw is configure to run locally and you can deploy it in your org. so technically all your data you can own them. Your logs go thru many steps. First thru ranking, only the flagged logs go to LLM usually 1-30% of your logs, LLM is used to understand the root cause and in creating a rich context incident ticket. LLM is not used to flag your logs. Currently we support standardized logs OTEL. so we can determine using our algo 99% of incidents.
The quality of your logs is critical. Our algo/LLM has no idea about your code but the "Logs". We currently push toward standardizing Otel based logs. You can read about it here https://opentelemetry.io/docs/specs/otel/logs/
Also developer configure the alerting conditions. LogClaw it automatically finds your incidents with out manual setting up alerting conditions on your log dashboard [splunk/datadog logs]
LogClaw algorithm is the moat here that flags logs first. Those only flagged usually less than 10% of the logs are analyzed by LLM. LLM is great at finding root cause if the logs are clear and detailed. So the LLM heavily depends on the quality of your logs. So if your logs are rich with info, it will have a better insights at understanding it.
I meant since this is designed to be deployed in companies private VPC, their data stays with them. Zero vendor data risk. Corrected it. Thanks for pointing it out.