Technology Tales

Merging language models offers a way to combine their strengths without retraining and Unsloth Studio provides a no-code, locally run interface to achieve this. The tool supports merging through methods such as SLERP for smooth blending of two models, TIES for resolving conflicts in multiple models and DARE for reducing redundancy by eliminating unnecessary parameters. Users can select models, configure merging settings and export the result in formats compatible with various inference tools.

The process involves installing the software, loading models, choosing a merge technique and exporting the final model, which can be saved locally or shared via Hugging Face. This approach allows practitioners to create efficient, combined models tailored to specific tasks, leveraging existing fine-tuned adapters or pre-trained weights while maintaining performance and reducing resource demands.

Claude Code is an agentic coding tool capable of reading codebases, editing files, running terminal commands and integrating across developer environments, but getting the most from it requires familiarity with the broader ecosystem of custom skills, subagents, hooks and reusable workflows that surround it. A growing number of GitHub repositories have emerged to help developers build that understanding, covering everything from fully structured agent harnesses and role-based AI team setups to curated directories of community tools, ready-made configuration templates and practical usage guides.

Some repositories focus on teaching developers how these systems are constructed from the ground up, while others collect system prompts and internal tool definitions from a range of AI coding products to aid comparison and research. Additional repositories track how Claude Code's internal prompts evolve across versions, offer libraries of specialised subagent definitions organised around real development tasks and provide structured workflow frameworks designed to reduce drift and improve consistency on larger projects. Together, these ten repositories represent a practical map of the Claude Code ecosystem for developers at varying levels of experience, from those just beginning to explore its capabilities to those building advanced multi-agent systems.

For those looking to develop practical skills in building AI agents, forking and running real repositories locally remains one of the most effective approaches.For those looking to develop practical skills in building AI agents, forking and running real repositories locally remains one of the most effective approaches. Ten open-source projects currently stand out in this space, each covering different aspects of agent design and deployment.

OpenClaw is a personal assistant framework supporting multiple messaging platforms with voice features and a broader skills ecosystem. OpenHands focuses on AI-driven software development and includes cloud, CLI and benchmarking capabilities. Browser-use enables agents to handle browser-based tasks such as form filling, research and navigation, while DeerFlow functions as a super-agent harness that coordinates sub-agents, memory and tools across longer, more complex tasks.

CrewAI offers a straightforward Python-first framework for multi-agent automation built independently of LangChain. LangGraph takes a lower-level approach, encouraging developers to think in terms of graphs, state and control flow for more robust agent runtimes. The OpenAI Agents SDK provides a lightweight set of building blocks for multi-agent workflows including hand-offs and tracing, and AutoGen, developed by Microsoft, covers orchestration patterns, agent conversation design and distributed multi-agent applications.

The remaining two have narrower but equally distinctive focuses. GPT Researcher is an autonomous deep-research agent that handles planning, browsing, synthesis and report generation in a single workflow. Letta places memory and stateful behaviour at the centre of its design, allowing agents to persist and evolve across interactions rather than resetting each time.

Five GitHub repositories offer practical entry points for anyone looking to understand quantum machine learning, a field that combines principles from quantum computing with machine learning methods. The largest of these, maintained by awesome-quantum-machine-learning, functions as a broad map of the field, covering fundamentals, algorithms, study materials and software, making it well suited to beginners. A smaller but more focused collection, awesome-quantum-ml, prioritises quality research papers and surveys for those who already have foundational knowledge and want to build academic depth.

For learners who prefer a hands-on approach, a repository tied to the book Hands-On Quantum Machine Learning With Python provides structured notebooks and scripts that allow readers to run experiments and observe how systems behave. A fourth repository concentrates on near-term quantum devices, the kind of noisy, limited hardware available today, and includes practical projects such as quantum support vector machines and quantum convolutional neural networks. Finally, the qiskit-machine-learning library, co-maintained by IBM and the Hartree Centre, offers a full suite of tools including quantum kernels, neural networks, classifiers and regressors, and integrates with PyTorch, making it the most suitable option for building complete professional pipelines. A suggested learning path involves beginning with the broader curated lists, progressing through research papers and guided notebooks and ultimately using the Qiskit library as a primary toolkit for more advanced experimentation.

Self-hosting large language models presents significant operational challenges that extend beyond initial expectations, encompassing hardware limitations, quantisation trade-offs, context window constraints, latency issues and the complexities of prompt engineering. While the promise of self-hosting includes control and reduced dependency on external APIs, practical implementation often reveals gaps in computational resources, with models requiring substantial VRAM and facing performance compromises when scaled.

Quantisation, though a common solution for hardware constraints, can degrade model accuracy in tasks requiring precision, necessitating empirical testing to balance efficiency and reliability. Context windows, frequently underestimated, impose practical limits on input length, compelling users to adopt strategies like aggressive chunking and selective content inclusion. Latency, particularly in interactive applications, can hinder usability, demanding investments in hardware or optimised frameworks.

Additionally, differences in prompt templates across models can lead to unexpected outputs, highlighting the need for careful configuration. Fine-tuning, while theoretically appealing, demands high-quality data and significant computational effort, often yielding better results from curated examples than large volumes of noisy data. Despite advancements in tooling that have lowered entry barriers, the process remains demanding, requiring patience and iterative refinement to achieve reliable performance.

Managing token usage in Claude Code effectively requires addressing context bloat rather than focusing solely on prompt length, as excessive session history, memory files and repeated instructions can significantly inflate costs. Practical strategies include selecting appropriate models based on task complexity, maintaining a concise CLAUDE.md file for persistent instructions, delegating verbose tasks to isolated subagents, specifying exact files and line ranges to avoid unnecessary exploration, proactively compacting sessions to remove redundant context, inspecting context usage to identify hidden inefficiencies and simplifying tooling setups to reduce overhead. By structuring workflows to limit irrelevant information and prioritising clarity, users can maintain performance while minimising expenses associated with token consumption.

The concept of "Bootleggers and Baptists," originally coined by economist Bruce Yandle in 1983 to describe how opposing groups can unite behind the same policy for entirely different reasons, applies with striking relevance to so-called data-driven decision making in modern organisations. On one side sit those who genuinely believe in letting evidence guide outcomes, people who update their views when data contradicts their assumptions and treat analysis as an open-ended process. On the other side are those who already have a preferred conclusion and work backwards, selectively choosing metrics, time ranges and figures that support their position while quietly omitting anything that does not.

The dynamic is particularly effective because both groups speak the same language and appear indistinguishable in practice, with the genuine believers lending credibility that the self-serving actors exploit as cover. The coalition often goes unnoticed because the principled participants believe the decision was reached honestly, unaware that the information placed before them was curated rather than comprehensive. Recognising the pattern requires attention to how people respond when data challenges their preferred outcome, whether the analytical process was genuinely exploratory before conclusions were drawn and who selected which figures were presented in the first place, since asking why particular data was chosen matters just as much as asking what it shows.

Real-world data rarely behaves as neatly as textbook examples suggest, and data scientists frequently encounter outliers, skewed distributions and unequal variances that cause standard statistical tests to break down. Rather than discarding problematic data, the solution lies in applying robust statistical methods, which are designed to produce reliable results even when classical assumptions are violated.

Using Python's Pingouin library and a wine quality dataset as a practical playground, three common failure scenarios are explored alongside their robust remedies. When normality tests fail, the Mann-Whitney U test offers a rank-based alternative to the t-test that neutralises the distorting influence of outliers. When paired measurements are not normally distributed, the Wilcoxon Signed-Rank Test provides a dependable substitute for the paired t-test by ranking absolute differences rather than relying on raw values. Finally, when unequal variances undermine a one-way ANOVA, Welch's ANOVA compensates by penalising high-variance groups to ensure fairer comparisons across categories. The broader point is that analytical competence is not about having clean data but about knowing which methods to reach for when the data refuses to cooperate.

Microsoft's Agent Framework, released in October 2025, extends both Semantic Kernel and AutoGen into a unified approach for building production agentic systems. A developer-focused initiative centred on the Boston Azure AI Group has produced training materials that walk through four interconnected technical areas built on top of it.

Safety comes first, treated as something measurable rather than assumed. A dual-model comparison runner sends identical prompts to guarded and unguarded deployments simultaneously, making behavioural differences concrete and observable, including the latency overhead that guardrails introduce.

Next is the Model Context Protocol, a standardised adapter that lets agents connect to external tools and services without requiring changes to the agent layer when underlying infrastructure changes. A particularly useful insight is that existing REST APIs can be wrapped in an MCP bridge and made accessible to agents without any modification to the back-end code.

Workflow orchestration then examines three patterns: sequential processing, concurrent fan-out to specialist agents and human-in-the-loop approval. The last of these is treated not as a fallback but as a deliberate, first-class pause in the workflow, making AI-assisted processes auditable in high-stakes environments.

The final area addresses the limitations of standard retrieval-augmented generation by moving to an agentic approach in which an orchestrator routes queries to specialist agents optimised for different retrieval strategies. This allows the system to handle yes/no questions, counting queries and multi-hop reasoning that a single retrieval pipeline handles poorly.

Taken together, the four areas move from behaviour observation through architecture construction to system operation. That progression covers what separates a working prototype from something genuinely deployable.

TOON offers a more efficient alternative to JSON for structured data input in large language model pipelines by reducing token overhead through compact, tabular formatting that eliminates repetitive field names while preserving data integrity. Designed for scenarios involving repeated structured records such as user entries or support tickets, it streamlines input without requiring changes to existing JSON-based systems, with the recommendation to retain JSON for application logic and outputs while using TOON for prompt context. Its effectiveness depends on data structure, making it most beneficial for uniform arrays of objects rather than deeply nested or irregular data and practical adoption involves testing token savings, latency and model performance in specific workflows before implementation.