I spent the last couple of weeks working hands-on with OpenClaw, running everything through Ollama. As an AI engineer tired of unpredictable cloud costs and rate limits, I wanted to push toward a fully local setup, but here’s the honest recap of what actually happened, what I built, broke, and learned.

First I got the full stack running: Gateway, Control UI, Browser Relay, and the Chrome extension. The architecture makes sense once you use it. Gateway coordinates everything, agents handle the reasoning, and the UI is mainly for observation. Browser control relies on the extension relay for tab attachment, profile switching, and session management. I debugged why Firefox threw persistent WebSocket 1008 unauthorized errors while Chrome connected smoothly , each browser requires its own auth token.

The biggest focus was making the agent responsible. I created one called Sage and built in ethical constraints from the beginning. No autonomous job applications, no sensitive actions without explicit approval. I added approval gates before major steps and implemented refusal logic with polite but firm escalation messages when boundaries are hit. It was useful to turn human-in-the-loop concepts into working code rather than just discussing them.

For models, I started with cloud-backed ones through Ollama (like kimi-k2.5:cloud) because they respond quickly and handle tool calling reliably. I tried switching to fully local models (Qwen variants, Llama, Phi, etc.) to eliminate any external dependency, but on my 8 GB RAM CPU-only machine the responses for each message took ages, often way too slow to be practical for iterative testing or real workflows. Even smaller quantized models felt sluggish, and the whole thing became frustrating. At this point I’ve settled on using the cloud Ollama providers with limited usage to keep things moving while staying mostly local where possible. Running OpenClaw smoothly with decent performance on local inference seems to demand more powerful hardware (better CPU, more RAM, ideally a GPU), which makes it feel expensive for a hobby-level setup right now.

The TUI model selector is convenient with keyboard controls once you learn it, but I ran into annoying issues like hidden filter states and checkbox toggles that didn’t behave reliably. In the end I edited the config directly to lock in what worked best for my current flow.

Browser automation was solid in Chrome after proper extension setup. Install, port verification, tab attachment lifecycle, it all became straightforward. One surprise was how silent the agent can seem during browser actions. Clicks and navigation happen visually without much text feedback, so it feels quieter than pure reasoning steps.

Logs were essential for debugging. I quickly learned to distinguish agent process failures from gateway issues, missing tokens, unattached tabs, LLM timeouts, or UI disconnects. Restart procedures are now routine instead of random reboots.

What I gained as an engineer:

Real experience integrating UI, backend, browser extension, and tools into a functional system A mostly local end-to-end agentic setup under my control (with cloud model fallback for speed) Clear view of local LLM practical limits: inference speed, quality, and RAM/CPU demands on modest hardware Hands-on responsible AI implementation: coded boundaries, approval gates, refusal patterns Debugging across WebSockets, networking, browser quirks, and model provider behaviors

If you’re exploring agent frameworks or local-first AI, OpenClaw is interesting to experiment with. It’s capable and the community is growing, but expect to balance performance tradeoffs depending on your hardware.

Anyone else using it with Ollama? Are you sticking to cloud providers for better speed, or have you found ways to make local models usable on average machines? Any hardware upgrades or config tweaks that helped? Curious what setups people are running.

Politics is a game of power and influence, and unfortunately, it is also a game of ego. We have seen numerous times in history when politicians have used their power to fulfill their egoistic desires. The recent name changes in Andhra Pradesh are a clear example of how politicians can use their power to fulfill their personal desires, disregarding the sentiments of the people and the achievements of great leaders.

The government of Andhra Pradesh has recently changed the viewpoint of A P J Abdul Kalam to YSR’s viewpoint and even changed the name of NTR Health University to YSR Health University. These decisions have raised many questions about the motive behind them. Can a political leader change any existing name of an organization, place, or anything else to his father’s name just because he is the father of the chief minister? The answer to this question is not straightforward, as it depends on the context and the reason behind the change.

In this case, the good or bad deeds done by Y S Rajashekar Reddy do not matter. Many great leaders in history have not received the recognition they deserve, and it is not fair to use their power to fulfill personal desires. A P J Abdul Kalam is one of the most eminent persons in the world, and his achievements and greatness cannot be overlooked. He was a scientist, a philosopher, and a great humanitarian. He served as the President of India from 2002 to 2007 and made significant contributions to the fields of science and technology. His views on education, youth empowerment, and national development have inspired millions of people across the world.

Changing the names of universities and viewpoints to fulfill personal desires is not only disrespectful but a waste of taxpayers’ money during the process. The government should focus on improving the quality of education and healthcare, creating employment opportunities, and enhancing the overall development of the state.

The recent name changes in Andhra Pradesh are not justifiable, and they reflect the egoistic and selfish motives of the politicians in power. If the chief minister loves his father so much, he can change the names of his personal business such as Sakshi TV, Sakshi newspaper, Bharathi cement, etc., to YSR TV, YSR newspaper, and YSR cement. But, using government machinery to fulfill personal desires is not acceptable.

In conclusion, it is high time for politicians to put aside their egoistic desires and work towards the betterment of society. The name changes in Andhra Pradesh are a clear example of how politicians can use their power to fulfill their personal desires. We need leaders who can inspire us with their achievements, rather than using them for their personal gains. Let us hope that the government of Andhra Pradesh will reconsider its decisions and work towards the development of the state, rather than wasting time and money on meaningless name changes.

— Raviteja Gonnabathula

When approaching a machine learning problem, the first step is to understand the problem you are trying to solve and the data you have available to solve it. This involves defining the objective of your model and identifying the features in your dataset that will be relevant for achieving that objective.

Next, you need to select a model that is appropriate for the problem you are trying to solve. This will involve considering the type of problem you are trying to solve (classification, regression, etc.), the size and quality of your dataset, and any specific requirements or constraints of your problem.

Once you have selected a model, the next step is to train it on your dataset. This involves splitting your data into training and validation sets, and using the training set to tune the parameters of your model. This process of training and validating the model is important for ensuring that your model is able to generalize to new data, and for identifying and addressing any overfitting or underfitting issues.

After training your model, the final step is to evaluate its performance on the validation set and, if necessary, make any adjustments to the model or the data to improve its performance. Once you are satisfied with the performance of your model, you can use it to make predictions on new data.

Overall, the key to successfully solving a machine learning problem is to carefully and thoroughly understand the problem and the data you are working with, to select an appropriate model and carefully tune its parameters, and to regularly evaluate and improve the performance of your model

2. The Recession of the Early 1990s (1990–1991): This recession was caused by a combination of high-interest rates, an overvalued dollar, and a slowdown in consumer spending. The recession began in July 1990 and officially ended in March 1991. The US economy contracted by 0.5%, and unemployment rose to 7.8%, the highest rate since 1983.

3. The Recession of 2001 (2001–2002): This recession was caused by the bursting of the dot-com bubble and the terrorist attacks of September 11, 2001. The US economy shrank by 0.3%, and unemployment rose to 6.3%. This recession also marked the first time in American history that the

#recession #analytics #history

Random Sampling

Simple Random Sampling:

Simple Random Sampling is the most primary sampling technique where the samples are randomly picked. Every entity in the population(N) has an equal opportunity to get selected once or more than once.

Stratified Random Sampling :

In Stratified Random Sampling population is divided into non-overlapping groups called strata, then samples are picked from each of the non-overlapping groups.Stratified Random Sampling reduces the sampling error which occurs when the sample does not represent the population. In stratified random sampling where strata are homogeneous within and heterogeneous all together. Strata are assigned to demographic variables, such as sex, socioeconomic class, geographic region, religion etc. Stratified random sampling can be either proportionate or disproportionate. Proportionate stratified random sampling occurs when the percentage of the sample taken from each stratum is proportionate to the percentage that each stratum is within the whole population.

Systematic Random Sampling :

The goal of the Systematic Random Sampling is to reduce the sampling cost and ease the process rather than reducing sampling error, every k_th entity is selected to produce a sample size of n from the population size N.

Cluster or Area Sampling :

In Cluster or Area Sampling population is divided into non-overlapping groups called strata, In stratified random sampling where strata are homogeneous within where as in Cluster or Area Sampling strata are heterogeneous within. Strata are assigned to towns, companies, homes, colleges, areas of a city, and geographic regions. Sometimes the clusters are too large, and a second set of clusters is taken from each original cluster. This technique is called two-stage sampling.

Nonrandom sampling

Convenience Sampling :

In convenience sampling, entities that are readily available, near by or willing to participate will be selected.

Judgment Sampling :

Judgment sampling occurs when elements selected for the sample are chosen by the judgment of the researcher.

Quota Sampling :

In Quota Sampling technique specific population sub groups such as demographic details or geographic details are used as strata. A non random approach is used to select the samples fro one stratum until the quota of samples is filled. Generally, a quota is based on the proportions of the sub classes in the population. In this case, the quota concept is similar to that of proportional stratified sampling. Quotas often are filled by using available, recent, or applicable elements.

Snowball Sampling :

Snowball sampling finds the entities based of the reference of initial entities. the initial entity is selected such that the profile matches with the study.

References :

1. Business Statistics For Contemporary Decision Making, Ken Black

-Ravi Teja Gonnabathula