Robelkidin

lmao codex vs Claude going to be like bmw vs Mercedes type of rivalry for the next decade

openAI closed down Sora too? How are they spending that much on compute. Anthropic seems realistic.

what do think AI will be in 5 years. currently CODING is solved.

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

check for more info https://get.alitagpt.com it contains landing and marketing, pricing, blogs page

we have 1000+ apps, but only 117+ that are non-technical friendly with simple to connect are rolled out.

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.

110+ apps widely used

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.

shout out to OpenClaw.ai [https://github.com/openclaw/openclaw] contributors. Amazing work ;)

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/

Please upvote if you lie our idea: https://www.producthunt.com/products/logclaw

And YES LogClaw.ai is able to injest terabytes of logs a day.

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]

Analyzing logs is not a LLM Foundation model issue.

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'm waiting for Anthropic's Email. I guess it is not as important as OpenClaw.

Appreciate it. try logclaw, we need your software insights on 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.

Thanks for pointing out. Try logclaw and we need your input instead,

We have Algorithm in place. Our system is capable of injesting terabytes of logs first it goes thru an algorithm that ranks every logs. ti simple put this, majority of logs are successful request or health checks or similar are ranked low and not sent to llm. Only those flagged above certain threshold are then analyzed by retrieving all the logs associated with it and analyzed for second time with LLM if its worthy of a ticket.

Yes LogClaw does that. It has ranking algorithm if anomaly is worthy of a ticket it will give you metrics blast radius and evidence logs. all the logs related to that issue. Similar incidents are grouped into one ticket.

LogClaw capable of injesting terabytes of logs a day. Our algorithm simply ignores successful request lifecycles which can help reduce the strains in analyzing terabytes of logs. Our algorithm then ranks and flags potential logs. later on we retrieve all logs associated with that log and analyze it more based on metrics if its worthy of a ticket/incident.

ALL existing product simply let you set up alerting system, and that alerting system is manually done by you. still un-expected issue can arise. LogClaw is not altering system. you just send all your logs, its capable of injecting terabytes of logs per day, and it automatically ignores all the successful logs, and it works on the uncaught exceptions, errors from all services, infrastructure itself.

Exactly. Incidents happen with uncaught issue. A simple of database query slowness or out of memory ...etc can cause your "perfectly designed feature" to cause P1. So it is super convienet for a system that invests all of your logs and monitors it for you. No need to customary set up alerts or trace traceIDs, connect logs through out micro-services.

It's designed to be SOC 2 compliant with your existing infra. You can spin up local Ollama instead of Claude/openAI APIs. But if you can use external Claude/OpenAI APIs over local Ollama [in-cluster llm].

The problem is a developer spending time to set up alerts for their new feature. I have done it many times on splunk yet that is so inconvenient. it's limited to what the developer expects the error are. example setting up status code based alert on a feature. and what happens if error alerts. A developer has to manual trace logs for a bunch of traceID. LogClaw wants to solve this issue. LogClaw 24/7 monitors your logs no need to set up alerts. when error a rise it will create a ticket with all logs for a particular traceId. No spending time on splunk/datadog log dashboard. Besides that, most of incidents happen unplanned errors on production. Those planned ones a developer has already set up a graceful way of handling them. What happens if your feature works right, but it happens to be it used frequently and Out of memory, or database queries slows, or external api exhausted ...etc and causing the error. There are many unplanned errors that LogClaw will monitor. LogClaw injects all the logs so it knows what's happening through out your whole codebase.

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