miercuri, 22 iulie 2026
marți, 21 iulie 2026
Tools : ... publicly available 4K visualizations of NASA.
NASA Scientific Visualization Studio is based out of the Goddard Space Flight Center (GSFC), located in Greenbelt, Maryland. The core studio consists of a team of (approximately) 15 visualizers, some of whom have been working with the studio for almost 30 years. The SVS's visualizers specialize in a wide variety of disciplines — astronomy, planetary science, climatology, cartography, and 3D modeling (to name a few) — but are united by a common love of making science accessible.
See the official website.
luni, 20 iulie 2026
News : Blender 5.2 LTS - Showcase Reel.
Five days ago Blender 3D team comes with this video, about the Blender 5.2 LTD version.
duminică, 19 iulie 2026
sâmbătă, 18 iulie 2026
Tools : testing LiteRT on static website with codesandbox.io online tool.
This simple project on codesandbox.io creates a responsive web interface for a LiteRT-powered image classifier that enables users to load machine learning models and process images directly within the browser.
Labels:
2026,
codesandbox.io,
development,
javascript,
programming,
tool,
tools,
web development
News : Spatialography on the VRChat.
Spatialography is a coined term that links Spatial （denoting space） with -graphy （denoting modes of depiction）․ Technologies such as photogrammetry and Gaussian splatting have brought new means to record and reconstruct real-world places․ Just as photography and film evolved from mere documentation into expressive media‚ so too will media that handle space develop techniques that go beyond simple recording․ This world exhibits a series of prototype works created with questions in mind˸ how can these techniques be used as modes of expression? which methods are suited to the format of space? is it possible to make works that seem to speak through space? Spatialography is not a single work title; rather‚ it is a term we use to denote the ongoing practice and inquiry of transforming recorded space into expressive form․
See the VRChat for this technologie.
News : Lazarus IDE version 4.8 - 06062026
Lazarus is a Delphi compatible cross-platform IDE for Rapid Application Development. It has variety of components ready for use and a graphical form designer to easily create complex graphical user interfaces.
See the official website.
The last version 4.8 was at 06062026.
Labels:
2026,
2026 news,
development,
Lazarus,
news,
programming
News : Build Without Limits on Roblox.
...
Today, we’re announcing Build, a new mobile-first creation tab within the Roblox app, and a new suite of AI-powered tools within Studio for creators of every level. On July 28, we’ll begin testing these new agentic tools. With Build and Studio, creators can delegate the parts of development that don’t require their full attention.
...
vineri, 17 iulie 2026
Tools : Understanding the hidden productivity trap in modern LLM development.
Today, this post comes with content on how to develop when using development as a tool.
In the context of modern AI and LLM-based software engineering, Yak Shaving has mutated from a traditional infrastructure nuisance into a highly sophisticated, resource-consuming technical trap. Classic yak shaving usually involves deep-diving into OS package dependencies, resolving library conflicts, or rewriting compiler flags just to implement a minor feature. In contrast, AI Yak Shaving operates at the meta-layer of AI orchestration. It is the insidious process of building hyper-complex pipelines, custom routing frameworks, and hyper-optimized vector architectures for the sake of the infrastructure itself, completely isolating the developer from the core product value and functional outcome.
For a Development Manager or Lead Architect, identifying and mitigating this behavior is critical. When engineering velocity drops to zero while architectural diagrams grow exponentially, your team is knee-deep in an AI yak shave. Below is an exhaustive, technical analysis of how this manifests across modern AI development and how to architect your way out of it.
1. The RAG Architecture Rabbit Hole and Vector Store Sprawl
Retrieval-Augmented Generation (RAG) is arguably the most fertile ground for AI Yak Shaving. What should conceptually begin as a straightforward context injection mechanism frequently devolves into an over-engineered distributed database engineering project.
- The Pipeline Trap and Chunking Obsession: Developers often spend weeks isolated in a sandbox environment experimenting with advanced chunking strategies. They build recursive character text splitters, semantic paragraph clusterers, and parent-child document relationships before evaluating if the model actually needs that level of granularity. They write complex mathematical models to determine the absolute perfect overlap percentage (e.g., matching 15% vs 20% tokens), forgetting that modern LLMs possess high structural tolerance for slightly messy or redundant contexts.
- Embedding Over-Engineering: This manifests when days are lost benchmarking text-embedding-3-small against bge-m3, or attempting to fine-tune a custom embedding model on a minuscule corporate dataset. Teams spend precious hours writing internal benchmarking scripts to evaluate vector distance metrics like Cosine Similarity, Euclidean Distance, and Dot Product. They perform these micro-optimizations before verifying if the underlying data has even been properly cleaned, parsed, or structured for consumption.
- Vector Store Sprawl: The developer starts with a simple, in-memory vector database like Chroma or a local FAISS index. Within days, under the premise of future scalability, they migrate to Qdrant, then to Pinecone, and finally to Milvus. The engineering focus shifts completely away from text retrieval and entirely toward tuning HNSW (Hierarchical Navigable Small World) graph parameters, configuring M-values, adjusting ef_construction sizes, and writing complex metadata filtering layers. The task is no longer about answering user queries; it has become a distributed systems infrastructure project.
- The Modern Strategic Reality: The emergence of massive, production-grade Long Context Windows (ranging from 1 Million to over 2 Million tokens in models like Gemini 1.5 Pro) has fundamentally altered this landscape. A significant portion of RAG yak-shaving is now structurally obsolete for early-stage or mid-sized products. Instead of wasting developer cycles building a complex, fragile retrieval pipeline that introduces chunking errors, you can often stream the entire raw documentation, codebase, or dataset directly into the LLM’s context window. This approach leverages the model's native attention mechanism to find information, which is frequently more reliable, drastically faster to deploy, and eliminates weeks of infrastructure maintenance.
2. Model Hopping, Quantization, and Infrastructure Churn
When developers focus on the inference engine rather than the inference logic, they lose sight of the business value.
- The Hardware and Quantization Trap: Engineers spend days trying to squeeze a specific large-parameter open-weights model into a specific local VRAM boundary. They debug complex llama.cpp compilation build flags, benchmark the semantic degradation of 4-bit GGUF quantization versus 8-bit EXL2 configurations, and write custom python drivers to handle model offloading between CPU and GPU.
- Registry and Deployment Misery: The team gets caught up juggling Ollama registry versions, managing local vLLM or Hugging Face TGI (Text Generation Inference) Docker containers, and debugging local KV cache allocations or context shifting logic.
- The Shave: The developer is no longer building features, designing user interfaces, or validating product-market fit. They have inadvertently transitioned into a full-time Inference Engine Operator. They are optimizing system-level memory layouts for a model whose prompt structure could have been re-written or optimized in ten minutes to run flawlessly on an abstracted, managed cloud API.
- The Modern Strategic Reality: Unless your enterprise operates under hard, legally binding offline data privacy mandates, or has reached a scale where API costs strictly justify capital expenditure on dedicated GPU clusters, local-first infrastructure maintenance is a massive velocity sink. Managed serverless APIs abstract away the hardware misery, offering sub-second latencies and auto-scaling capabilities out of the box, allowing your team to focus exclusively on upstream application features.
3. The Illusion of Agentic Over-Orchestration
The industry fascination with autonomous agents has introduced a highly deceptive form of technical debt through frameworks like LangGraph, CrewAI, and AutoGen.
The typical symptom of this shave is the premature construction of hyper-complex state machines, endless circular tool-calling loops, and multi-agent hierarchical frameworks to solve problems that are inherently linear. Developers design Manager Agents that delegate tasks to Writer Agents and Researcher Agents, who then report back to an Editor Agent.
When this system is executed, it inevitably breaks down due to compounding token errors, state mutation bugs, and JSON parsing failures. The developer then spends their entire week writing robust, nested Self-Correction Loops and validation logic to force the agents to format their output correctly. They are debugging the orchestration framework rather than addressing the fact that the original task was too broad or ill-defined for an autonomous agent loop.
The industry is rapidly shifting away from chaotic, non-deterministic agent swarms and moving toward structured, predictable Agentic Workflows. This means abandoning complex, self-correcting autonomous loops in favor of linear, deterministic code pipelines. By utilizing Structured Outputs (forcing the LLM to adhere strictly to a Pydantic schema or JSON schema via constrained grammar at the inference level), you completely bypass the need for tool-output parsing scripts and self-correction loops. If a standard Python script can handle the step-by-step logic, do not use an agent swarm.
4. Breaking Free from Evaluation Paralysis
Evaluation is a critical component of shipping reliable software, but in AI development, it frequently transforms into the ultimate manifestation of procrastination and over-engineering.
Before a single customer has interacted with the application, the engineering team decides they cannot ship without a comprehensive, automated evaluation framework. They begin building a highly intricate LLM-as-a-judge system. This involves writing meta-prompts to evaluate prompt variants, generating synthetic evaluation datasets, and spending weeks analyzing precision, recall, and F1-scores for minor prompt modifications.
The fundamental trap here is that if you do not have actual production users, your synthetic evaluation dataset is merely a reflection of your own internal engineering assumptions. You are optimizing a test suite for a product whose real-world utility has zero validation. The modern approach dictates prioritizing Production Observability over upfront evaluation perfection. Deploying native telemetry tools (such as LangSmith, Phoenix, or OpenInference) allows you to log real user interactions, capture actual system failures, and build a high-fidelity golden dataset based on real-world usage rather than theoretical sandbox hypotheses.
5. The Hidden Cost: Cognitive Debt and Organizational Fatigue
The true damage of AI Yak Shaving is not merely measured in wasted hours or inflated cloud computing invoices; it is measured in the accumulation of Cognitive Debt and the degradation of engineering velocity.
- Context Switching Degradation: When a developer must simultaneously manage vector database indices, debug Docker network configurations for local model hosting, trace state mutations across an agent framework, and write PyQt6 UI wrappers, their capacity for deep focus on the core business problem is completely destroyed.
- The Opaque Black Box: Every meta-layer added to the AI stack introduces an abstraction that conceals underlying bugs. When the application returns a garbage output, the debugging surface area is massive. Is the error caused by a bad prompt, an improper chunking strategy, a vector distance calculation mismatch, an agent routing failure, or a model quantization bug? The system becomes untestable and terrifying to refactor.
- The Loss of Shared Technical Theory: As autonomous loops and complex pipelines multiply, the human development team loses their shared mental model of how the software operates. Engineers become hesitant to modify code for fear of triggering an un-traceable cascade of non-deterministic model failures across the pipeline. Product innovation completely freezes.
6. Strategic Management: The Complexity Budget and Blueprint
To systematically eliminate AI Yak Shaving from your organization, you must enforce a strict, tier-based Complexity Budget for every new feature development cycle:
- Tier 0 (Zero-Abstraction Baseline): Implement the feature using a single, well-structured API call combined with direct Prompt Engineering. Utilize system prompts and few-shot examples. If this satisfies 80% of the target acceptance criteria, development stops here. Do not add code.
- Tier 1 (Deterministic Augmentation): If Tier 0 fails due to context limits or data access, implement a basic context injection (load the file directly into the context window) or a simple, single-index vector lookup. Use native Structured Outputs to ensure predictable data schemas.
- Tier 2 (Orchestrated Complexity): Only when Tier 1 exhibits proven, measurable performance bottlenecks that directly harm the end-user experience are you allowed to introduce advanced architectures like multi-stage prompt chains, custom RAG pipelines, or specialized local model deployments.
As a Development Manager, apply the 60-Minute Zero-Latency Deployment rule. If an engineer cannot take a new product requirement and stand up a crude, functional, end-to-end prototype using a basic API call within 1 hour, your engineering organization is actively shaving a yak. Strip away the frameworks, bypass the infrastructure, eliminate the local registries, and force the system back to its simplest mathematical primitive: an input string, a model call, and an output string. Ship a functional product first, capture real telemetry, and engineer complexity only when driven by hard production data.
Labels:
2026,
artificial intelligence,
development,
programming,
tool,
tools
joi, 16 iulie 2026
Tools : Sinemarka Bookmark Toolkit | bookmarks, broken links, redirects, and bot-protected pages ...
I'm Bilal Gümüş, a web developer based in Istanbul working under the Sinemarka brand. I build interfaces that feel calm, considered and fast — the kind of software that gets out of the way and lets people do their work.
Sinemarka Bookmark Toolkit is a comprehensive, local-first Firefox extension designed to help users clean, analyze, and reorganize years of accumulated bookmarks. It performs deep scanning of both bookmarks and open tabs, identifying broken links, redirects, bot-protected pages, and timeouts. All operations run entirely on the user's device, with no cloud connection, no account requirement, and no external data transfer. This makes the extension ideal for users who prioritize privacy while managing large bookmark collections.
The extension includes a powerful health-checking system that evaluates each bookmark's HTTP status. It distinguishes between genuinely broken links and benign HTTP-to-HTTPS upgrades, ensuring that functional pages are not mistakenly flagged. Cloudflare or bot-protected pages are marked as protected instead of broken, providing more accurate diagnostics. Long scans can be paused and resumed, and users can configure per-URL timeouts to fine-tune performance.
Organization features are extensive. Users can organize bookmarks by domain, select specific domains for cleanup, find duplicates, and fix redirects in bulk. Broken links can be archived into a dedicated folder, allowing users to revisit them anytime. Individual folders can be renamed, moved, or scanned independently, and the extension supports undoing the last change to prevent accidental data loss. These tools make it possible to transform a chaotic bookmark library into a structured, efficient system.
Import and export capabilities are robust. Users can generate styled HTML reports in their active Firefox theme and language, export bookmarks in browser-importable Netscape HTML format, or choose Markdown and JSON exports. The extension also supports importing bookmarks from HTML files, making it compatible with other browsers and external bookmark managers. This flexibility is valuable for users who migrate between browsers or maintain multiple bookmark archives.
The interface is designed for usability and accessibility. It offers Light, Dark, and Rosé themes, adjustable fonts, and the option to use a side panel or a full-tab interface. The extension is available in English, Türkçe, and Español, making it accessible to a wide international audience. Because everything runs locally, users benefit from fast performance and complete privacy.
- Local-first scanning of bookmarks and open tabs for broken links, redirects, and bot-protected pages.
- Accurate HTTP status detection with special handling for Cloudflare-protected pages and HTTPS upgrades.
- Pause and resume functionality for long scans, with configurable per-URL timeouts.
- Domain-based organization with per-domain selection for targeted cleanup.
- Bulk duplicate detection and redirect fixing for large bookmark collections.
- Archiving of broken links into a dedicated folder for later review.
- Folder-level operations including rename, move, scan, and undo.
- Export options including styled HTML, Netscape HTML, Markdown, and JSON.
- Import support for browser bookmark HTML files.
- Customizable interface with multiple themes and adjustable fonts.
- Full privacy with no backend, no cloud, and no data collection.
Sinemarka Bookmark Toolkit is particularly valuable for users who have accumulated thousands of bookmarks over many years. These collections often contain outdated links, duplicates, and redirects that reduce browsing efficiency. By performing deep analysis and offering advanced cleanup tools, the extension helps users restore order and maintain a healthy bookmark ecosystem. It is especially useful for researchers, developers, content creators, and anyone who relies heavily on bookmarks for daily work.
The extension's ability to scan open tabs alongside bookmarks provides an additional layer of utility. Users can identify broken or redirected pages they currently have open, making it easier to manage active browsing sessions. This feature is helpful for users who work with many tabs simultaneously and need to ensure that all resources remain accessible.
Because Sinemarka Bookmark Toolkit supports exporting structured reports, users can share or archive detailed analyses of their bookmark collections. These reports can be used for documentation, migration, or long-term storage. The availability of Markdown and JSON exports also makes the extension suitable for technical workflows, automation, or integration with external tools.
Overall, Sinemarka Bookmark Toolkit stands out as one of the most advanced bookmark management extensions available for Firefox. Its combination of privacy, performance, deep scanning, flexible organization, and rich export options makes it a powerful solution for both casual users and professionals. By using this extension regularly, users can maintain a clean, organized, and efficient bookmark library that remains useful for years to come.
For users who frequently import bookmarks from multiple browsers, synchronize across devices, or maintain large research archives, Sinemarka Bookmark Toolkit provides a reliable and comprehensive way to manage link health, structure, and long-term accessibility. Its local-first design ensures that all operations are fast, secure, and fully under user control.
- Ideal for researchers, developers, and heavy bookmark users who require long-term stability.
- Suitable for users migrating between browsers or consolidating multiple bookmark archives.
- Provides accurate diagnostics for link health and redirect behavior.
- Supports large-scale cleanup operations without compromising privacy.
- Offers a professional-grade interface with multiple customization options.
With its extensive feature set and privacy-focused architecture, Sinemarka Bookmark Toolkit is a valuable addition to any Firefox installation. It helps users reclaim control over their bookmarks, eliminate clutter, and maintain a clean browsing environment. Whether you are dealing with a few hundred bookmarks or tens of thousands, the extension provides the tools needed to analyze, organize, and preserve your digital resources effectively.
miercuri, 15 iulie 2026
News : online game procedual Townscaper by Oskar Stålberg.
See this game named Townscaper by Oskar Stålberg artist.
News : Android Studio Quail 2 stable version with multi-task and the Android Studio AI agent.
Android Studio Quail 2 is now stable and ready for you to use in production, bringing a shift to your IDE with concurrent agentic workflows, natively integrated memory leak profiling, and context-aware crash remediation. Whether you are performing a sweeping architectural overhaul, tracing a memory leak, or resolving a critical production crash, Android Studio keeps you anchored in your workspace by reducing manual friction.
In Android Studio Quail 2, we've been hard at work redesigning Agent Mode from the ground up. This new architecture provides better performance, offers more flexibility for decomposing complex tasks, and improves the suite of internal tools the agent uses to do its work.
Labels:
2026,
2026 news,
android,
development,
google,
news,
programming
marți, 14 iulie 2026
News : Assembly language and how hard can be , even on hacking area ...
Because, I work with the assembly programing language you can see these two videos ...
Gedare Bloom - youtube channel.
Daniel Hirsch - youtube channel.
Labels:
2026,
2026 news,
assembly,
development,
news,
programming,
video,
videos
Tools : vast the infrastructure layer ...
Vast is the infrastructure layer where AI agents autonomously design, procure, and optimize their own compute. API-native provisioning. Real-time pricing. Per-second billing.
See the official webpage.
Labels:
2026,
2026 news,
artificial intelligence,
news,
vast
luni, 13 iulie 2026
duminică, 12 iulie 2026
News : monolith-terrain an interactive, real-time 3D terrain map.
An interactive, real-time 3D terrain map in the style of a vintage USGS topographic sheet, crossed with a sci-fi FUI overlay. Load real-world elevation data for anywhere on Earth, or generate procedural mountain ranges — then explore them with contour lines, hypsometric tinting, survey grids, spot elevations, clickable peak markers, radar scans, and cinematic camera tours.
Tools : Total Commander - android application.
The old Total Commander is now on android google play.
Labels:
2026,
tool,
tools,
Total Commander
sâmbătă, 11 iulie 2026
vineri, 10 iulie 2026
News : Three.js Water Pro
Three.js Water Pro.
Real-time, physically-based ocean rendering for Three.js WebGPU.
Labels:
2026,
2D,
3D,
development,
graphic,
graphics,
programming,
three.js
News : Blender 5.2 LTS - Release Candidate!
Blender 5.2 LTS has reached Release Candidate! 🚀
Now's the time to put it to the test and report any issues. Let's make this release as stable as possible!
See the official website.
joi, 9 iulie 2026
News : LiteRT.js, Google's high performance Web AI Inference from Google
We are excited to announce LiteRT.js, a JavaScript binding of LiteRT for running AI directly inside the web browser. By bringing the trusted on-device inference library LiteRT to the web, web developers can now run ML and AI models with maximum performance entirely locally. This means enhanced user privacy, zero server costs, and ultra-low latency for real-time experiences. For developers with existing .tflite models, LiteRT.js makes deployment to mobile and desktop web browsers smoother than ever, serving as a powerful evolution from TensorFlow.js for executing .tflite models.
See the official webpage.
Labels:
2026,
2026 news,
google,
javascript,
LiteRT,
news,
web development
miercuri, 8 iulie 2026
News : PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation
State-of-the-art single-image 3D reconstruction methods often rely on complex hybrid architectures and loss functions, or compress geometry into latent spaces in order to leverage pre-trained latent diffusion models. In this work, we show that such architectural overhead and intricate loss formulations are unnecessary. We introduce a minimalist pixel-space Diffusion Transformer, built on a plain ViT, that operates directly on raw 3D point map patches and is conditioned on image tokens from a pre-trained DINOv3. Unlike existing latent diffusion approaches, we train our diffusion backbone entirely from scratch, eliminating the need for point map tokenizers. Despite its simplicity, our approach surpasses complex latent-based diffusion models while remaining significantly simpler than hybrid alternatives. Notably, it produces sharper geometric structure and is more robust in highly ambiguous regions, such as transparent objects.
Labels:
2026,
2026 news,
huggingface,
news
News : Laguna XS 2.1 from Ollama.
Laguna XS 2.1 is a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token designed for agentic coding and long-horizon work on a local machine.
See the official website.
Labels:
2026,
2026 news,
artificial intelligence,
news,
ollama
marți, 7 iulie 2026
Tools : simple guide of the most relevant terms, structured as a specialized in development.
Today, with a little help of artificial intelligence I created this a comprehensive synthesis of the most relevant terms, structured as a specialized guide.
In the software development field, jargon and slang are extremely rich, evolving rapidly alongside new technologies.
1. Code Quality and Technical Debt
These terms are used in professional environments to evaluate the state of a codebase and the trade-offs made during development:
- Technical Debt: Decisions made to deliver a feature quickly at the cost of lower quality, which will require additional effort to correct later.
- Code Smell: Symptoms or patterns in the code that suggest a deeper design problem, even if the code is functional.
- Spaghetti Code: Source code with a complex and incomprehensible control structure, often the result of chaotic development.
- Code Rot: The process by which a software system becomes increasingly difficult to maintain and more prone to errors as it is modified over time.
- Maintainability Index: A software metric that measures how easy it is to maintain, modify, and understand a codebase.
- Brittle Code: Code that breaks as soon as you change a single setting or a small part of the input.
2. Slang for Poor Quality (Beyond Slop)
- Boilerplate: Repetitive, standard code that must be written to make a feature work but adds no unique logical value.
- Cargo Cult Programming: The practice of copying and implementing code snippets or design patterns without understanding why they are used.
- Quick and Dirty: A solution implemented rapidly, without a solid structure or maintainability, usually with the intention of being refactored later.
- Kitchen Sink: Code or a project where everything has been added, without a clear direction, resulting in an oversized and unmanageable codebase.
- Hack: An inelegant solution that fixes something for the moment but creates significant risks in the long term.
- Low-effort slop: A slang term borrowed from creative and online communities to describe mass-generated content that lacks added value or attention to detail.
3. Processes, Errors, and Productivity
- Yak Shaving: The situation where you start a simple task but realize you have to solve a minor problem, then another, and another, until you lose hours doing things completely unrelated to the main objective.
- Bikeshedding: The phenomenon where the team spends hours discussing trivial details instead of focusing on the critical architecture of the project.
- Heisenbug: A bug that disappears or changes its behavior when you try to investigate or debug it.
- Race Condition: An error that occurs only under very specific timing conditions, making it extremely difficult to reproduce in a controlled manner.
- Zombie Code: Code that is no longer used by anyone in the application but has not been deleted because programmers are afraid that if they remove it, something will break.
- Rubber Ducking: The method of explaining your code, line by line, to an inanimate object. The act of explaining often helps you find the logical error yourself.
4. The AI Paradigm: Risks and Automation
- Prompt Engineering: Although it has become a technical term, in the programming community it is sometimes used ironically to describe the process of trying to get the AI to produce something useful through repeated trial and error.
- AI-in-the-Middle: The situation where a programmer no longer understands the logical flow of their application because they have delegated too much to an AI assistant, losing control over the code architecture.
- Hallucination: Used to describe code or a model that has a high probability of generating false information, logical errors, or calls to non-existent libraries.
- Stochastic Parrot: A critical term describing language models that repeat statistical patterns without understanding the meaning or context of what they are generating.
- Shadow Code: Code introduced into a project without being fully understood or reviewed.
- Copy-Paste Programming: The practice of taking code without understanding how it works or how it interacts with the rest of the system.
5. AI Yak Shaving (AI Infrastructure Pitfalls)
- The RAG Architecture Rabbit Hole: Instead of a simple context injection, the developer becomes obsessed with chunking strategies, vector distance metrics, and migrating between vector databases.
- Model Hopping: Occurs when the focus shifts to the provider or model architecture rather than the inference logic, spending hours fine-tuning quantization parameters or local registry versions.
- Agentic Over-Orchestration: Getting lost in Agentic Frameworks, building complex state machines and multi-agent hierarchies for a task that is essentially a linear sequence of inputs.
- Evaluation Paralysis: Building systems to evaluate LLM outputs using another LLM before shipping any feature, thereby optimizing the evaluation system instead of validating the product value.
6. Testing and Validation in the AI Era
- Golden Master Testing: A fundamental testing technique originating from the music and film industry. It involves running a system with a set of inputs and capturing the output in a known, correct state. When AI modifies the code, the output is compared to this Master to verify if the changes are safe.
- Mutation Score: A metric that quantifies the AI drift, representing how many changes were required to make the AI-generated code behave like the Golden Master.
- LLM-as-a-Judge: Using a second, distinct AI instance that receives the input, output, and code to evaluate if the output is logically consistent with the requirements.
- Property-Based Testing: Instead of checking for an exact match, the AI is prompted to verify if essential properties hold true.
- Self-Healing Tests: A framework where a failed test triggers the AI-Author again with instructions to rewrite the code based on the specific error, effectively allowing the AI to debug itself.
- Circular Hallucination: The risk that an AI-Judge evaluates the code positively simply because it was trained on the same data as the Author, sharing the same biases or errors.
7. Governance and Structured Logging
- ADRs (Architecture Decision Records): The gold standard for recording decisions; this is where you document a Hack or a Quick and Dirty solution, transparently acknowledging the Technical Debt you are incurring.
- Structured Logs (JSONL): For an AI to learn from development history, logs must be structured rather than stored as unstructured text.
- Remediation Engine: Feeding your structured logs into a more capable AI model using a Meta-Prompt to analyze recurring patterns of logic errors and suggest system prompt updates or coding standards.
- Human-in-the-Loop (HITL): The ultimate safety standard; the developer defines Trust Checkpoints. If a module passes the AI-Judge and Golden Master tests repeatedly, it becomes Trusted Code and is merely monitored rather than re-verified.
Labels:
2026,
artificial intelligence,
development,
programming,
tool,
tools
News : Grok Voice Agent Builder Beta.
Voice Agent Builder is now live in beta: a no-code platform for production voice agents with Grok Voice. Every account includes a free phone number. Create a voice agent in under 2 minutes, then call it from your browser.
It’s for operators and developers who want high-volume production voice agents without building the stack from scratch. You get telephony, knowledge retrieval, tools, guardrails, and observability in one place. You can also keep what you already have: bring existing phone numbers, wire your APIs, or connect MCP servers.
Most voice stacks stitch together speech-to-text, a language model, and text-to-speech, often across different providers. Every hop adds latency and new failure modes. Voice Agent Builder is one interface built for Grok Voice, tightly coupled to the model.
See the official website.
Labels:
2026,
2026 news,
artificial intelligence,
news,
xAi
Google Apps Script : example of transition from a Google Sheet to a formatted Google Doc.
This script that automates the transition from a Google Sheet to a formatted Google Doc. Here is a breakdown of what this solution does:
Custom Menu Integration: The script adds a Custom Menu directly into your Google Sheet interface. With a single click, you can trigger processes like fetching data or generating reports, making the tool incredibly easy to use without touching the code.
This example is based on this URL : https://www.planetpython.org/opml.xml.
Intelligent Data Processing: It doesn't just copy text; it parses RSS and Atom feeds, extracts titles and links, and prepares them for your document. It intelligently handles the HYPERLINK formulas you use in Sheets, translating them into real, clickable links in your final document.
Automatic Folder Management: One of the most useful features of this script is its ability to keep your Drive organized. Instead of cluttering your root directory, the script automatically identifies the folder where your source spreadsheet is located and creates the new document right there.
Built-in Troubleshooting: I have included logging functionality throughout the code. If a link is malformed or a feed fails to load, the script logs exactly what happened, allowing you to debug issues quickly via the Apps Script Dashboard.
Let's see the source code for Google Apps Scripts:
function onOpen() {
SpreadsheetApp.getUi()
.createMenu('Custom Menu')
.addItem('Read OPML PlanetPython', 'fetchAndProcessFeed')
.addItem('Download Selected Content', 'downloadSelectedContentWrapper')
.addItem('Send Sheet via Email', 'sendSheetViaEmail')
.addToUi();
}
function fetchAndProcessFeed() {
const dateTime = Utilities.formatDate(new Date(), Session.getScriptTimeZone(), "yyyy-MM-dd_HH-mm");
const spreadsheet = SpreadsheetApp.getActiveSpreadsheet();
let sheet = spreadsheet.getSheetByName(dateTime);
if (!sheet) {
sheet = spreadsheet.insertSheet(dateTime);
} else {
sheet.clear();
}
sheet.appendRow(["Date", "Source", "Title", "Select"]);
const opmlUrl = "https://www.planetpython.org/opml.xml";
const opmlContent = UrlFetchApp.fetch(opmlUrl).getContentText();
const opmlDoc = XmlService.parse(opmlContent);
const outlines = opmlDoc.getRootElement().getChild("body").getChildren("outline");
outlines.forEach(outline => {
const text = outline.getAttribute("text")?.getValue().replace(/"/g, '""').replace(/[<>&]/g, '') || "";
const xmlUrl = outline.getAttribute("xmlUrl")?.getValue() || "";
try {
const feedContent = UrlFetchApp.fetch(xmlUrl).getContentText();
const feedDoc = XmlService.parse(feedContent);
const root = feedDoc.getRootElement();
if (root.getName() === "rss") {
processRssFeed(root, text, xmlUrl, sheet);
} else if (root.getName() === "feed") {
processAtomFeed(root, text, xmlUrl, sheet);
}
} catch (e) {
Logger.log(`Error processing feed ${text} (${xmlUrl}): ${e}`);
}
});
}
function processRssFeed(root, source, xmlUrl, sheet) {
const items = root.getChild("channel")?.getChildren("item") || [];
items.forEach(item => {
const title = item.getChild("title")?.getValue().replace(/"/g, '""').replace(/[<>&]/g, '') || "";
const pubDate = item.getChild("pubDate")?.getValue() || "";
const link = item.getChild("link")?.getValue().replace(/[<>&]/g, '') || xmlUrl;
sheet.appendRow([
pubDate,
`=HYPERLINK("${xmlUrl}", "${source}")`,
`=HYPERLINK("${link}", "${title}")`,
""
]);
});
}
function processAtomFeed(root, source, xmlUrl, sheet) {
const entries = root.getChildren("entry") || [];
entries.forEach(entry => {
const title = entry.getChild("title")?.getValue().replace(/"/g, '""').replace(/[<>&]/g, '') || "";
const updated = entry.getChild("updated")?.getValue() || "";
const link = entry.getChild("link")?.getAttribute("href")?.getValue().replace(/[<>&]/g, '') || xmlUrl;
sheet.appendRow([
updated,
`=HYPERLINK("${xmlUrl}", "${source}")`,
`=HYPERLINK("${link}", "${title}")`,
""
]);
});
}
function downloadSelectedContentWrapper() {
downloadSelectedContent();
}
async function downloadSelectedContent() {
const dateTime = Utilities.formatDate(new Date(), Session.getScriptTimeZone(), "yyyy-MM-dd_HH-mm");
const spreadsheet = SpreadsheetApp.getActiveSpreadsheet();
const sheet = spreadsheet.getActiveSheet();
const data = sheet.getDataRange().getValues();
const doc = DocumentApp.create(`Content_${dateTime}`);
const folderId = DriveApp.getFileById(spreadsheet.getId()).getParents().next().getId();
DriveApp.getFileById(doc.getId()).moveTo(DriveApp.getFolderById(folderId));
let contentAdded = false;
const body = doc.getBody();
for (let i = 1; i < data.length; i++) {
const row = data[i];
if (row[3] == 1) {
const sourceMatch = row[1].toString().match(/^=HYPERLINK\("([^"]+)",\s*"([^"]+)"\)$/);
const source = sourceMatch ? sourceMatch[2] : row[1].toString().replace(/[<>&]/g, '') || "Unknown source";
const titleMatch = row[2].toString().match(/^=HYPERLINK\("([^"]+)",\s*"([^"]+)"\)$/);
const title = titleMatch ? titleMatch[2] : row[2].toString().replace(/[<>&]/g, '');
const link = titleMatch ? titleMatch[1] : "";
Logger.log(`Processing ${title} with link: ${link}`);
try {
body.appendParagraph(source).setHeading(DocumentApp.ParagraphHeading.HEADING1);
const titleParagraph = body.appendParagraph(title).setHeading(DocumentApp.ParagraphHeading.HEADING2);
if (link) titleParagraph.setLinkUrl(link); // Titlu ca hyperlink
body.appendParagraph(""); // Spațiu între intrări
contentAdded = true;
Logger.log(`Added ${title} with link`);
} catch (e) {
Logger.log(`Error processing ${title} (${link}): ${e}`);
body.appendParagraph(source).setHeading(DocumentApp.ParagraphHeading.HEADING1);
const titleParagraph = body.appendParagraph(title).setHeading(DocumentApp.ParagraphHeading.HEADING2);
if (link) titleParagraph.setLinkUrl(link);
body.appendParagraph("");
contentAdded = true;
}
}
}
if (!contentAdded) {
Logger.log("No content was added to the document.");
body.appendParagraph("No content was selected or available.");
}
}
function sendSheetViaEmail() {
const spreadsheet = SpreadsheetApp.getActiveSpreadsheet();
const sheet = spreadsheet.getActiveSheet();
const sheetName = sheet.getName();
const data = sheet.getDataRange().getValues();
let htmlContent = `
<html>
<head>
<title>${sheetName}</title>
</head>
<body>
<h1>${sheetName}</h1>
<table border="1">
<tr>
<th>Date</th>
<th>Source</th>
<th>Title</th>
<th>Select</th>
</tr>
`;
data.forEach((row, index) => {
if (index > 0) {
const date = row[0] || "";
const sourceMatch = row[1].toString().match(/^=HYPERLINK\("([^"]+)",\s*"([^"]+)"\)$/);
const sourceUrl = sourceMatch ? sourceMatch[1] : "";
const sourceText = sourceMatch ? sourceMatch[2] : row[1].toString().replace(/[<>&]/g, '');
const titleMatch = row[2].toString().match(/^=HYPERLINK\("([^"]+)",\s*"([^"]+)"\)$/);
const titleUrl = titleMatch ? titleMatch[1] : "";
const titleText = titleMatch ? titleMatch[2] : row[2].toString().replace(/[<>&]/g, '');
const select = row[3] || "";
htmlContent += `
<tr>
<td>${date}</td>
<td><a href="${sourceUrl}">${sourceText}</a></td>
<td><a href="${titleUrl}">${titleText}</a></td>
<td>${select}</td>
</tr>
`;
}
});
htmlContent += `
</table>
</body>
</html>
`;
const mimetype = "application/epub+zip";
const containerXml = `
<?xml version="1.0"?>
<container version="1.0" xmlns="urn:oasis:names:tc:opendocument:xmlns:container">
<rootfiles>
<rootfile full-path="OEBPS/content.opf" media-type="application/oebps-package+xml"/>
</rootfiles>
</container>
`;
const contentOpf = `
<?xml version="1.0" encoding="UTF-8"?>
<package xmlns="http://www.idpf.org/2007/opf" unique-identifier="bookid" version="2.0">
<metadata xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:opf="http://www.idpf.org/2007/opf">
<dc:title>${sheetName}</dc:title>
<dc:identifier id="bookid">urn:uuid:${Utilities.getUuid()}</dc:identifier>
<dc:language>en</dc:language>
</metadata>
<manifest>
<item id="content" href="content.html" media-type="application/xhtml+xml"/>
</manifest>
<spine>
<itemref idref="content"/>
</spine>
</package>
`;
const blobs = [
Utilities.newBlob("mimetype", "text/plain", "mimetype").setContentType(mimetype),
Utilities.newBlob(containerXml, "application/xml", "META-INF/container.xml"),
Utilities.newBlob(contentOpf, "application/xml", "OEBPS/content.opf"),
Utilities.newBlob(htmlContent, "application/xhtml+xml", "OEBPS/content.html")
];
const zipOutput = Utilities.zip(blobs, `Sheet_${sheetName}.epub`);
MailApp.sendEmail({
to: "catalinfest@gmail.com,catafest@yahoo.com",
subject: `Sheet activ: ${sheetName}`,
body: "Vezi atașat sheet-ul activ în format EPUB.",
attachments: [zipOutput]
});
}luni, 6 iulie 2026
Tools : Visual Code Ollama extension for developers.
Use Ollama models in VS Code Chat.
The Ollama extension adds models from your running Ollama server to the VS Code model picker.
Ollama 0.17.6 or newer is recommended for cloud model sign-in and richer model metadata. Older Ollama versions may still work for local models.
Requirements
- Visual Studio Code 1.120 or newer
- Ollama installed and running
- At least one local or cloud model available in Ollama
Labels:
2026,
ai tools,
development,
programming,
tool,
tools,
Visual Studio Code
duminică, 5 iulie 2026
News : Google - View Space Locations by NASA.
I found a map with all view space locations on google by nasauofl@gmail.com.
Labels:
2026,
google,
google map,
maps,
NASA
News : Security - OpenCVE teams can use automations.
We’re excited to introduce a major evolution in how OpenCVE helps teams manage CVEs: Automations.
If you use OpenCVE today, you already know the value of tracking CVEs that match your vendors and products. But as subscriptions grow, so does the noise. Every update, every score change, every new reference can demand attention.
Automations give you control over when, why, and how OpenCVE reacts to the CVEs in your projects.
Vulnerability monitoring is not just about detection. Security, SOC, DevSecOps, and engineering teams need to prioritize, triage, and act without drowning in alerts.
Until now, much of that logic lived inside notification configurations: event filters, CVSS thresholds, delivery rules, all bundled together. That worked, but it was hard to extend. You could notify, but you couldn’t easily assign a CVE, change its status, or build a scheduled digest with the same flexibility.
See the official website.
sâmbătă, 4 iulie 2026
vineri, 3 iulie 2026
joi, 2 iulie 2026
miercuri, 1 iulie 2026
News : 500 free points - AI Cloud Plans.
Start creating with Reallusion AI Services for free. Choose the plan that best fits your needs, with a unified AI Points system that works across AI Studio, Video Mocap, and Headshot Image Generation.
See the official webpage.
Labels:
2026,
2026 news,
3D,
artificial intelligence,
design,
graphic,
graphics,
news,
Reallusion
Tools : use powershell scripting to fix python bad run windows store .
Use this powershell source code will fix the python command when start the windows store.
# Run first
# Set-ExecutionPolicy RemoteSigned -Scope CurrentUser
Add-Type -AssemblyName System.Windows.Forms
Add-Type -AssemblyName System.Drawing
# --- Create Form ---
$form = New-Object System.Windows.Forms.Form
$form.Text = "Python Path Fixer"
$form.Size = New-Object System.Drawing.Size(500, 300)
$form.StartPosition = 'CenterScreen'
# --- Label & Path Input ---
$label = New-Object System.Windows.Forms.Label
$label.Text = "Python Directory:"
$label.Location = New-Object System.Drawing.Point(10, 20)
$label.AutoSize = $true
$form.Controls.Add($label)
$txtPath = New-Object System.Windows.Forms.TextBox
$txtPath.Location = New-Object System.Drawing.Point(10, 45)
$txtPath.Size = New-Object System.Drawing.Size(460, 20)
$txtPath.Text = "C:\PythonInstall"
$form.Controls.Add($txtPath)
# --- Log Box ---
$txtLog = New-Object System.Windows.Forms.TextBox
$txtLog.Multiline = $true
$txtLog.ScrollBars = 'Vertical'
$txtLog.Location = New-Object System.Drawing.Point(10, 80)
$txtLog.Size = New-Object System.Drawing.Size(460, 130)
$txtLog.ReadOnly = $true
$form.Controls.Add($txtLog)
# --- Fix Button ---
$btnFix = New-Object System.Windows.Forms.Button
$btnFix.Text = "Check and Fix Path"
$btnFix.Location = New-Object System.Drawing.Point(10, 220)
$btnFix.Size = New-Object System.Drawing.Size(460, 30)
$btnFix.Add_Click({
$pythonFolder = $txtPath.Text
$pythonExe = Join-Path $pythonFolder "python.exe"
$txtLog.Text = "Checking: $pythonExe`r`n"
if (Test-Path $pythonExe) {
$txtLog.AppendText("[+] Python found. Updating PATH...`r`n")
$currentPath = [Environment]::GetEnvironmentVariable("Path", "User")
$pathParts = $currentPath -split ";" | Where-Object { $_ -ne $pythonFolder -and $_ -ne "" }
$newPath = "$pythonFolder;" + ($pathParts -join ";")
[Environment]::SetEnvironmentVariable("Path", $newPath, "User")
$txtLog.AppendText("[OK] Priority set successfully!`r`nPlease restart your terminal.")
} else {
$txtLog.AppendText("[ERROR] python.exe not found in the specified directory!")
}
})
$form.Controls.Add($btnFix)
[void]$form.ShowDialog()
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