sawlon

SnagTrace | Deterministic AI Agent Fault Detection Library

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SnagTrace is an open-source Python library that helps developers identify common failures in AI agent execution traces using deterministic analysis. Instead of relying on another language model to judge whether an agent failed, it detects structural issues such as execution loops, invalid tool arguments, and cost budget overruns, then reports the exact step where the problem occurred. The library is designed to integrate with modern agent frameworks such as LangChain and LangGraph while remaining lightweight, reproducible, and easy to extend. Overview Debugging AI agents can be difficult because a single execution may involve hundreds of tool calls, multiple agents, and long execution traces. While existing observability platforms provide tracing and replay, developers often still need to manually inspect traces to locate the source of a failure. SnagTrace focuses on failure types that can be detected reliably without an LLM. By analyzing execution traces with deterministic rules, it produces consistent results and helps reduce the time required to investigate common agent failures. Features Key Capabilities Loop Detection Detects repeated tool invocations and recursive execution patterns using configurable sliding-window analysis. Tool Argument Validation Identifies invalid tool inputs, schema mismatches, truncated arguments, and parsing failures before they propagate through an agent workflow. Cost Budget Monitoring Tracks cumulative execution cost and reports the precise step where a configured budget threshold is exceeded. Technologies Design SnagTrace follows a deterministic approach to agent debugging. Each detector analyzes execution traces independently without calling an external language model. This makes diagnoses reproducible, inexpensive to run, and suitable for automation in development and CI workflows. The detector system is modular, allowing additional rule-based detectors to be added without changing the core architecture. Performance The library is designed for efficient analysis of large execution traces while keeping detectors stateless and easy to test. For long-running agents, traces can be analyzed periodically or after execution completes to avoid unnecessary overhead. My Role This project was designed and developed independently. My work included: Outcome SnagTrace provides a lightweight way to detect common AI agent failures without introducing additional LLM-based analysis. It is intended for developers who want deterministic, reproducible diagnostics that can be integrated into local development workflows or automated pipelines.

How to Set Up a Local AI Coding Assistant with Ollama and Continue.dev in VS Code (2026 Guide)

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Imagine having a powerful AI pair programmer that never sends your code to the cloud, works completely offline, and costs nothing to run. With the mature ecosystem of local large language models (LLMs) and the right tools, this isn’t just a fantasy; it’s a reality in 2026. In this step-by-step guide, I’ll show you how to build a fully private, local AI coding assistant using Ollama and Continue.dev inside Visual Studio Code. By the end, you’ll have an intelligent autocomplete, chat, and refactoring companion running 100% on your machine. No API keys, no data leaks, no subscription fees. Why a Local AI Coding Assistant in 2026? Cloud-based coding assistants like GitHub Copilot and ChatGPT have undeniable convenience, but they come with serious trade-offs: recurring costs, dependency on internet connectivity, and most critically your source code is processed on external servers. For many developers, especially those in finance, healthcare, or working on proprietary projects, that’s a dealbreaker. Local LLMs have exploded in capability. Models like DeepSeek-Coder-V2, Code Llama 3, and Codestral can now rival cloud models in code understanding and generation, all while running on consumer GPUs. When paired with the right integration, you get a fast, privacy-respecting assistant that lives entirely within your editor. What Are Ollama and Continue.dev? Ollama is an open-source tool that lets you run LLMs locally with a single command. It handles model downloading, quantization, and provides a simple API—ideal for stitching into other tools. Continue.dev is an open-source VS Code (and JetBrains) extension that connects your editor to any LLM—cloud or local. It’s designed to be model-agnostic, so you can plug in Ollama models for both chat and autocomplete with minimal configuration. Together, they form the perfect stack for a local AI coding assistant. Prerequisites Before we start, make sure you have the following: Step 1: Install Continue.dev Extension in VS Code Open VS Code, go to the Extensions view (Ctrl+Shift+X / Cmd+Shift+X), and search for Continue. Install the extension by Continue Development, Inc. (the open-source one). Once installed, you’ll see a new sidebar icon (the Continue logo). After installation, you may need to reload VS Code. Step 2: Configure Continue to Use Ollama The magic happens in Continue’s configuration file. Open the VS Code Command Palette (Ctrl+Shift+P / Cmd+Shift+P) and run: This opens ~/.continue/config.json (or config.ts if you use TypeScript). We’ll edit the JSON to connect to your locally running Ollama instance. Replace or add the following inside the “models” array. Here’s a minimal setup that uses deepseek-coder-v2 for both chat and autocomplete: Explanation: Important: Save the config file (Ctrl+S). Continue will automatically reload the configuration. Step 3: Start Ollama and Test the Connection Make sure Ollama is running in the background. On most systems, it runs as a service after installation. You can test it by visiting http://localhost:11434 in your browser—you should see the message Ollama is running. Now, inside VS Code, open the Continue sidebar (the chat panel). You should see the model “DeepSeek-Coder-V2 (Local)” listed at the top. Type a simple query like: “Write a Python function to check if a string is a palindrome.” If everything is working, the model will respond with code and an explanation, all generated locally. Step 4: Using Autocomplete With tabAutocompleteModel configured, inline completions will automatically appear as you type in any file. By default, they show up in a ghost text style, and you can press Tab to accept. If you find the suggestions too frequent or too slow, tweak the “tabAutocompleteOptions” in your config: delay is the milliseconds after you stop typing before the suggestion appears—increase it if you’re on a slower machine. maxTokens limits the length of completions, preventing overly long, slow responses. Step 5: Advanced Configuration – Separate Models for Chat and Completion For a more refined setup, you can assign a larger, more capable model for chat (where you need detailed explanations) and a smaller, snappier model for autocomplete. Here’s a recommended pairing: With codellama:13b for deep chat discussions and codegemma:2b for lightning-fast inline suggestions, you’ll get the best of both worlds. Experiment with models from the Ollama library to find your sweet spot. Step 6: Optimizing Performance Running LLMs locally can be resource-intensive. Here’s how to keep things snappy: Step 7: Going Beyond Code – Custom Slash Commands Continue supports slash commands that let you perform actions like /edit, /comment, /test directly from the chat. You can even define custom commands. For example, create a command that asks the local model to explain a highlighted block of code in simple terms: This can dramatically speed up code review and learning, all done locally. Troubleshooting Common Issues “Model not found” error in ContinueCheck that you’ve actually pulled the model with ollama list. Also, ensure the model field in config matches exactly (including tag if any, like deepseek-coder-v2:latest). “Failed to connect to localhost:11434”Ollama may not be running. Restart it with ollama serve (or check your system service). If you’re using WSL2 on Windows, make sure you’ve exposed the port correctly. Autocomplete is too slow or laggySwitch to a smaller model for tabAutocompleteModel, increase delay, or reduce maxPromptTokens. Also, confirm GPU acceleration is active by looking at GPU utilization while it’s generating. Insufficient memory errorsTry a smaller quantization or a model with fewer parameters. codegemma:2b runs well even on 8GB RAM without a GPU. Why This Setup Beats Cloud-Based Assistants Conclusion In 2026, there’s no reason to compromise your privacy for AI-powered coding. With Ollama and Continue.dev, you can spin up a sophisticated, local AI coding assistant in less than 15 minutes. The models are smarter, the tooling is seamless, and the performance is better than ever. Start with deepseek-coder-v2, experiment with different configurations, and enjoy a coding companion that’s truly your own. If you run into any snags, the vibrant communities of both projects are just a GitHub issue away. Happy coding—offline and fully private.

GLM-5.2: Built for Long-Horizon Tasks (And Why It Matters)

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glm-5-2-long-horizon-tasks-ai

Let’s be completely honest for a second. Most AI models today have the attention span of a goldfish. You can ask them to write a quick email or summarize a short webpage, and they do a great job. But what happens when you ask an AI to plan a six-month marketing strategy, write a piece of enterprise software, or conduct deep, multi-stage academic research? They get lost. They forget your original instructions halfway through. Sometimes, they just start making things up to cover up the fact that they lost the thread. That is exactly the problem the newest iterations of General Language Models are trying to solve. Enter GLM-5.2. If you are tired of babysitting your AI tools, you need to understand what this model is doing differently. GLM-5.2 isn’t just another incremental update meant to make chatbots a little faster. It is specifically built for long-horizon tasks. But what does that actually mean, and why should you care? The Problem with Short Attention Spans To understand why GLM-5.2 is a big deal, we have to talk about how we currently use AI. Most of what we do with standard chatbots falls under the category of short-horizon tasks. You ask for a recipe, a joke, or a blog post outline. The AI pulls from its training data, generates a response, and the interaction is over. But real work isn’t like that. Real work is messy and complex. A long-horizon task is a multi-step objective that requires sustained reasoning over time. Imagine asking an AI to read a massive financial report, identify hidden market trends, draft a comprehensive strategy document based on those trends, review its own draft for logical inconsistencies, and format the final output for a corporate presentation. To do this successfully, an AI can’t just have a good memory. It needs something closer to executive function. It needs to keep the ultimate goal in mind while executing dozens of micro-steps without losing the plot. Historically, large language models have failed spectacularly at this because of something called context decay. The further along a conversation gets, the more the AI’s intelligence degrades. It starts taking shortcuts and ignoring earlier instructions. How GLM-5.2 Tackles Long-Horizon Reasoning GLM-5.2 represents a fundamental shift in how these models are architected. Instead of focusing purely on being a snappy, conversational assistant, the developers decided to tackle the hardest problem in artificial intelligence: sustained, complex reasoning. The model does this by making context highly usable. Having a massive context window—meaning the AI can ingest a lot of text at once—is like having a giant desk. But if your desk is completely covered in clutter, you still can’t find what you need. GLM-5.2 uses advanced attention mechanisms to actually retrieve and weigh the importance of information buried deep within massive datasets. It doesn’t just read a whole codebase; it remembers how the code at line ten affects the logic at line ten thousand. Beyond just memory, GLM-5.2 is built with agentic workflows in mind. This means it doesn’t just sit there waiting for your next prompt like a glorified search engine. You can give it a high-level goal, and it will break that goal down into a web of sub-tasks. It can pause, evaluate its own progress, correct its own mistakes, and keep moving forward autonomously until the entire task is finished. What This Means for Your Day-to-Day Work Okay, enough about the underlying tech. What does this mean for how you actually work? If you are a developer, you know that coding isn’t just about writing text. It’s writing, compiling, hitting an error, reading logs, and fixing the bug. GLM-5.2 is designed to act more like an autonomous junior developer in this regard. You can give it a complex repository and ask it to migrate a database structure. It can read the code, write the migration, test it against the existing logic, fix the errors it finds, and output the final file without you having to hold its hand at every step. For researchers, lawyers, and data analysts, the implications are just as massive. These professions spend hundreds of hours cross-referencing documents. Because GLM-5.2 is built for extended reasoning, you can feed it dozens of legal briefs or medical studies and ask it to synthesize a final report. It will hold the arguments from the first document in its “mind” while comparing them to the arguments in the twelfth document, ensuring the final synthesis is cohesive. Even in project management, this changes the game. Imagine assigning an AI to manage a product launch. It can draft the timeline, assign tasks based on team roles, draft the communications for those tasks, and adjust the timeline dynamically as you feed it updates. The entire time, it remembers the original launch date and budget constraints without you having to remind it. Moving Past the Novelty Phase We are moving past the novelty phase of artificial intelligence. The wow factor of an AI writing a poem about a cat is officially over. Businesses and professionals don’t need AI that can write a clever limerick; they need AI that can sit down, do hours of deep work, and deliver a polished result. That is why GLM-5.2 matters. It signals a transition from conversational AI to actual workhorse AI. By focusing on long-horizon tasks, it bridges the gap between a helpful assistant and an autonomous agent. It’s not perfect, no model is, but it represents a massive leap toward AI that can actually handle the messy, complicated, multi-step realities of real human work. If you’ve been frustrated by AI tools that lose the thread halfway through a project, it’s time to look at what this new generation of models brings to the table. With its massive context retention and focus on agentic reasoning, it is finally giving us a glimpse of what happens when an AI learns how to truly focus. The goldfish era of AI is ending, and the era of deep, sustained, long-horizon work is just beginning.

Deep Research Agent – Autonomous AI Research Assistant

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ai agent

Abstract As Large Language Models (LLMs) evolve from simple text generators to reasoning engines, the focus of AI development has shifted toward agentic workflows—systems capable of autonomous planning, tool use, and self-correction. To explore the efficacy of modern orchestration frameworks, I engineered the Deep Research Agent: a fully autonomous system designed to perform iterative, multi-step research tasks. This project demonstrates a production-ready implementation of a cyclic graph architecture (LangGraph) utilizing the Groq API for high-throughput inference. The resulting system achieves professional-grade research synthesis with a marginal operating cost of $0.005 per query, proving that high-performance autonomous agents can be built cost-effectively using open-weight models. 1. System Overview: The Deep Research Agent The Deep Research Agent is not merely a wrapper for an LLM; it is a stateful application that mimics the workflow of a human analyst. Unlike zero-shot querying, this system employs an iterative “thought-loop” to refine information quality before generating a final response. Core Capabilities: 2. Technical Stack & Design Choices The architecture was chosen to maximize architectural flexibility while minimizing inference latency and operational costs. 3. Architectural Analysis: Cyclic Graph vs. Linear Chains A key engineering decision in this project was the implementation of a Cyclic Graph architecture over a traditional Linear Chain. 4. Engineering Implementation & Challenges The development process highlighted several critical aspects of building production-grade agents. A. State Management Implementation Effective state management is the backbone of any agentic system. I implemented a TypedDict structure with reducer operators to maintain context across iterations. This ensures that research findings are accumulated rather than overwritten during loops. Python B. Resilience and Error Handling To ensure robustness suitable for automated tasks, I implemented exponential backoff strategies for all external API calls. This prevents cascade failures during momentary latency spikes from search or LLM providers. Python C. Resource Optimization (Cost Analysis) A primary objective was to demonstrate the economic feasibility of running autonomous agents at scale. By optimizing the system prompt and pruning search results (limiting context window usage), the system achieves a 95% cost reduction compared to proprietary model APIs (e.g., GPT-4). Metric Standard API approach Deep Research Agent (Optimized) Cost Per Query ~$0.10 **~$0.005** Latency Variable < 3s (Inference) Architecture Black Box Open / Customizable 5. Conclusion & Future Scope This project validates that professional-grade AI agents do not require prohibitive budgets or closed ecosystems. By leveraging LangGraph for sophisticated orchestration and Groq for high-speed inference, I have engineered a system that is both autonomous and economically scalable. Future Research Directions: Repository: github.com/kazisalon/Deep-Research-Agent

Fine-Tuning LLaMA 3.1 for English–Nepali Medical Translation

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nepali translation

Medical information is only useful when people can understand it. While English has a large ecosystem of medical resources, high-quality medical translation for Nepali remains limited. General-purpose language models can translate everyday text reasonably well, but they often struggle with clinical terminology, dosage instructions, and domain-specific language. This project explores whether a large language model can be adapted for English–Nepali medical translation using parameter-efficient fine-tuning instead of retraining the entire model. The goal was to build a reproducible baseline that can serve as a foundation for future research in low-resource medical NLP. Why I Built This Many multilingual language models support Nepali, but most are trained for general translation rather than medical language. Clinical text introduces challenges that aren’t common in everyday conversations, including specialised terminology, formal writing style, and safety-critical information. Instead of relying on prompt engineering alone, I wanted to evaluate whether domain-specific fine-tuning could significantly improve translation quality while remaining feasible on consumer-grade hardware. Building the Dataset One of the biggest challenges was the lack of publicly available English–Nepali medical parallel data. To address this, I compiled a parallel corpus consisting of 58,682 aligned sentence pairs collected from publicly available medical resources, health education materials, question–answer datasets, and health-related articles. The dataset contains only publicly available information and excludes personally identifiable or protected health information. Model and Training The project uses Meta’s LLaMA 3.1 8B Instruct as the base model. Rather than fine-tuning all 8 billion parameters, I adopted a parameter-efficient approach using LoRA together with 4-bit NF4 quantization (QLoRA). This reduced GPU memory requirements while training only about 0.52% of the model’s parameters. Training was performed on a Tesla T4 (16 GB VRAM) provided by Google Colab over approximately 25–30 hours, demonstrating that domain adaptation can be achieved without access to expensive multi-GPU infrastructure. Evaluation To evaluate the model, I used two standard machine translation metrics: The fine-tuned model achieved: Metric Score BLEU 11.63 ChrF++ 34.65 Compared with the zero-shot LLaMA 3.1 baseline, this represents an 8.9× improvement in BLEU and a 2.1× improvement in ChrF++, showing that parameter-efficient fine-tuning substantially improves domain-specific translation performance. What I Learned The project reinforced that fine-tuning is only part of building a reliable translation system. Dataset quality, domain coverage, evaluation methodology, and error analysis all have a significant impact on the final results. Although the model preserves many medical terms and produces grammatically correct Nepali translations, it can still omit safety-critical information in some cases. For that reason, it should be treated as a first-draft translation assistant rather than a replacement for qualified bilingual medical professionals. Future Work There are several directions for extending this research:

Understanding F1 Score in Machine Learning

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f1 score machine learning

The F1 score is a crucial metric in the field of machine learning, particularly in the evaluation of classification models. It provides a balance between precision and recall, making it especially useful in scenarios where the class distribution is imbalanced. This document will delve into the definition, calculation, and significance of the F1 score, along with its applications in various domains. What is F1 Score? The F1 score is the harmonic mean of precision and recall. It is defined as follows: Where: The F1 score is then calculated using the formula: [ F1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} ] Importance of F1 Score The F1 score is particularly important in the following scenarios: Applications of F1 Score The F1 score is widely used in various domains, including: Conclusion In summary, the F1 score is an essential metric in machine learning that provides a balanced measure of a model’s precision and recall. Its significance is particularly pronounced in scenarios involving imbalanced datasets and varying costs of prediction errors. Understanding and utilizing the F1 score can lead to better model evaluation and selection, ultimately enhancing the effectiveness of machine learning applications.

Recursive Induction of Decision Trees: A Building Block of Random Forest

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Recursive Induction of Decision Trees

Decision trees are a fundamental building block in machine learning, particularly in the context of ensemble methods like Random Forest. A decision tree is a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. In machine learning, decision trees are used to classify or predict outcomes based on a set of input features.   Recursive Induction: The Core Process The process of building a decision tree is known as recursive partitioning or recursive induction. It involves the following steps: Key Concepts in Decision Tree Induction Advantages of Decision Trees Limitations of Decision Trees Conclusion Recursive induction is a powerful technique for building decision trees. By understanding the principles of feature selection, splitting criteria, and stopping conditions, you can effectively construct accurate and interpretable decision trees. While decision trees can be used as standalone models, they are often combined with other techniques like bagging and boosting to create more robust and powerful ensemble models like Random Forest.

Random Forest in Machine Learning

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Random Forest in Machine Learning

Random Forest is a versatile and robust machine learning algorithm that belongs to the family of ensemble learning methods. It combines multiple decision trees to create a more accurate and stable predictive model. How Random Forest Works Key Advantages of Random Forest Applications of Random Forest Limitations of Random Forest Conclusion Random Forest is a powerful and flexible machine learning algorithm that has proven its effectiveness in a wide range of applications. Its ability to handle large datasets, reduce overfitting, and provide feature importance makes it a valuable tool in the data scientist’s arsenal. By understanding its strengths and limitations, you can effectively apply Random Forest to solve complex machine learning problems.