GLM-5V-Turbo does native multimodal coding, balanced visual and programming capabilities, and deep adaptation for Claude Code and Claw Scenarios.

The model can understand design drafts, screenshots, and web interfaces to generate complete, runnable code, truly achieving the goal of "seeing the screen and writing the code.

GLM-5V-Turbo leads in benchmarks for design draft reconstruction, visual code generation, multimodal retrieval and QA, and visual exploration. It also performs exceptionally well on AndroidWorld and WebVoyager, which measure control capabilities in real GUI environments.

Regarding pure-text coding, GLM-5V-Turbo maintains stable performance across three core benchmarks of CC-Bench-V2 (Backend, Frontend, and Repo Exploration), proving that the introduction of visual capabilities does not degrade text-based reasoning.

The leading performance of GLM-5V-Turbo stems from systematic upgrades across four levels:

• Native Multimodal Fusion: Deep fusion of text and vision begins at pre-training, with multimodal collaborative optimization during post-training. We developed the next-generation CogViT visual encoder, reaching SOTA in general object recognition, fine-grained understanding, and geometric/spatial perception. We also designed an inference-friendly MTP structure to ensure high efficiency.
• 30 Task Collaborative RL: The RL stage optimizes over 30 task types simultaneously, covering STEM, grounding, video, and GUI Agents. This improves perception and reasoning while mitigating the instability often found in single-domain training.
• Agentic Data and Task Construction: To solve the challenge of scarce Agent data, we built a multi-level system ranging from element perception to sequence-level action prediction. We use synthetic environments to generate verifiable training data and inject "Agentic Meta-capabilities" during pre-training (e.g., adding GUI Agent PRM data to reduce hallucinations).
• Multimodal Toolchain Extension: Beyond text tools, the model supports multimodal search, drawing, and web reading. This expands the perception-action loop into visual interaction. Synergies with Claude Code and OpenClaw are enhanced to support full-loop task execution.

Source: Z AI

AI tools have dramatically changed how fast teams can launch websites. A landing page that once took days of drafting, feedback loops, and revisions can now be ready in a single session. But speed alone isn't the win most teams think it is.

The real problem shows up after launch.

Many AI-generated pages look polished at first glance — clean layouts, confident copy, logical structure. And yet they underperform in real traffic. Users land, read, and leave without converting. Why? Because fast output and reliable outcomes are two very different things.

The most common culprits:

Generic relevance. The page sounds professional but doesn't speak to anyone specific. Visitors can't tell if it's actually for them.

Shallow mechanism. The product is described, but not explained. "Powered by AI" tells users nothing about what actually happens or why it works.

Misplaced trust signals. Testimonials and proof exist — but they're buried below the fold, far from the bold claims that created doubt in the first place.

Chaotic iteration. Teams tweak headlines, layouts, and CTAs all at once, then have no idea what actually moved the needle.

The fix isn't better AI — it's a better system.

High-performing teams treat AI as a production amplifier, not a decision-maker. They still own positioning, claim validation, and release approval. AI handles drafting, variations, and repetitive formatting work. The distinction matters.

A practical structure that consistently works follows four questions in sequence: Who is this for and why now? How does it actually work? Why should I trust this? What do I do next? Every section on the page should be earning its place within that narrative.

Before generating any copy, the best teams write a short brief: one objective, one audience segment, one mechanism summary, and one intended action. This brief becomes the source of truth — for the AI prompt and for every human edit that follows.

Release gates matter too. Mobile should be treated as a strict requirement, not an afterthought. If a first-screen relevance check fails on small screens, the page doesn't ship.

And testing discipline separates teams that learn from teams that just move fast. One variable per release. One primary metric plus one guardrail. Clear notes on what changed and why.

For a detailed breakdown of the full 10-step workflow — including how to structure proof placement, CTA logic, and a 30-day implementation plan — the original guide on Unicorn Platform is worth reading in full: Building AI-Assisted Websites in 2026

The teams building durable growth with AI aren't the ones using the most tools. They're the ones running the clearest system.

When applications evolve, even small UI changes can unintentionally affect layouts, styles, or user experience. This is where visual regression testing becomes valuable. It focuses on detecting changes in the appearance of an application after updates, ensuring that the interface remains consistent and user-friendly.

Instead of checking functionality alone, this approach compares visual elements—such as layouts, colors, fonts, and spacing—before and after changes. By identifying differences, teams can quickly spot issues like misaligned components, broken layouts, or unintended design changes that might otherwise go unnoticed.

In practice, teams capture baseline snapshots of the interface and compare them with new versions after updates. These comparisons can be done manually or with automated tools that highlight even minor visual differences. This is especially useful for applications with complex user interfaces or frequent design updates.

Visual regression testing is often integrated into development workflows alongside other testing methods. It adds an extra layer of validation by ensuring that the product not only works correctly but also looks as intended across different devices and environments.

By incorporating visual regression testing into regular workflows, teams can maintain design consistency, catch UI issues early, and deliver a more polished and reliable user experience.

2026 has many layoff in tech companies till date. Below is list of major tech layoffs for past three months.

• ASML 1,700 people
• Atlassian 1,600 people
• Amazon 16,000 people
• Salesforce 1,500 people
• Epic Games 1,000 people
• Block 4,000 – 5,100 people
• WiseTech Global 2,000 people
• Oracle: 20,000 – 30,000 people
• Meta (Reality Labs) 1,500 people

Yupp AI launched in June 2025 was a San Francisco-based AI platform designed to compare over 500 AI models (like GPT-4, Claude, Gemini) side-by-side, allowing users to select the best response to queries or image generation requests. It operated as a, crowdsourced, free service, letting users earn rewards for evaluating and rating AI outputs to generate training data.

As per Co-founder and CEO Pankaj Gupta, the website will be up for another 15 days during which time users can download their chat data. New users won’t be able to sign up and existing users won’t be able to create new conversations after today.

Source: Pankaj Gupta

OpenAI raises $122 billion to accelerate the next phase of AI, says the company.

OpenAI was the fastest technology platform to reach 10 million users, the fastest to 100 million users, and soon the fastest to 1 billion weekly active users. Within a year of launching ChatGPT, they reached $1B in revenue. By the end of 2024 they were generating $1B per quarter. OpenAI are now generating $2B in revenue per month. At this stage, they claim that they are growing revenue four times faster than the companies who defined the Internet and mobile eras, including Alphabet and Meta.

The round was anchored by their strategic partners Amazon, NVIDIA, and SoftBank, with continued participation from OpenAI's long-term partner, Microsoft. SoftBank co-led the round alongside a16z, D. E. Shaw Ventures, MGX, TPG, and accounts advised by T. Rowe Price Associates, Inc.

Source: OpenAI

Hi fellow founders!

I built esotericAI as an experiment combining symbolic systems (tarot astrology) with modern AI and real astronomical calculations.

The idea started as a curiosity/ambition: could LLMs generate meaningful interpretations about symbolic/abstract systems given the right resouces/references?

Not the intention to prove anything, just exploring how technology and ancient symbolic systems work together and if it provides real value to people.

This project actually started during a hackathon. I didn’t like any of the ideas on idea lists/pools, so I ended up with something around two things I’ve always been interested in: technology and esoteric/symbolic systems.

Growing up, my family was very into things like tarot, astrology, I Ching, pendulums, and similar esoteric practices. I grew up around conversation about the universe, the cosmos, books about these things, palmistry, tarot readings during difficult moments, and a lot of discussions about cycles, energy, patterns, and how people try to interpret life through symbols.

Whether you believe in those things or not, I always found the symbolic structure behind them fascinating. My interest in astronomy and the science part, along with astrology and its symbolic part, and all the symbolic systems out there are part of my genuine curiosities, so the idea of combining tarot and astrology symbolism/real orbital math with AI interpretation felt like an interesting experiment, things like digital tarot are not new and are used since much longer, but now I could give it much more resources and richness.

Instead of hardcoding meanings, the app generates readings and cosmic insights dynamically from:

• tarot card combinations

• natal chart placements

• real-time planetary positions

• current transits

Some technical details in case you're interested:

• Frontend: React Vite SPA (no Next.js)

• Backend: Supabase (Postgres Edge Functions)

• AI: OpenAI API (used for interpretation, not calculation)

• Orbital math: custom calculations for planetary positions houses

• Localization: EN / PT-BR with locale-aware routing

• Hosting: Netlify Edge functions for SEO snapshots

For astrology, I didn't want to call external APIs, so I implemented:

• planetary positions from orbital elements

• local sidereal time

• ascendant / midheaven calculation

• aspect detection

• whole sign houses

For tarot, the system doesn't store fixed meanings. Each reading is generated from:

• card archetype

• position context

• question intent

• previous readings history

Some interesting challenges I ran into:

• grounding/framing LLM outputs when translations are inconsistent

• SEO issues with SPA bots (solved with edge HTML injection)

• Timezone / birth location precision for natal charts

• Keeping readings and journey chapters meaningful and to the point with so many potential interpretations and signals

• Preventing prompt injection in user questions

• I would say that being a solo founder is also a challenge by itself, hahah

This is still an indie project, but it turned into a full platform with:

• tarot readings (daily/ask a question/share a draw)

• natal chart blueprint with on demand current transits-based insights

• daily cosmic transits insights

• generated tarot tales based on trends

• energy archetype / personality generation of destined connections

Would love feedback, especially from people interested in:

• LLM structured inputs

• symbolic reasoning

• astrology math / orbital calculations

• Edge functions

• SPA SEO strategies

• Monetization and distribution for niche SaaS/webapps

Here is a demo video of its earliest stages:

https://www.loom.com/share/ec90a688118a4b63b20d0875471977fe

Happy to talk about any aspects of it.

Sanity testing plays a crucial role in modern software development by ensuring that recent code changes, bug fixes, or minor enhancements do not introduce new issues into an application. In fast-paced development environments where continuous integration and frequent deployments are common, sanity testing provides a quick and focused method to validate that specific functionality works as expected before moving to more extensive testing phases. Unlike full regression testing, which evaluates the entire system, sanity testing concentrates only on the modified components, saving both time and effort while maintaining software stability.

This type of testing is typically performed after minor updates, patches, or bug fixes when developers need quick confirmation that the recent changes did not negatively impact existing functionality. It helps teams detect critical issues early, preventing unstable builds from progressing further in the Software Development Life Cycle (SDLC). Because sanity testing is limited in scope and quick to execute, it allows development teams to maintain productivity without sacrificing quality.

Sanity testing is especially valuable in agile and DevOps environments where rapid releases are frequent. It provides immediate feedback, reduces testing cycles, and improves collaboration between developers and QA teams. By focusing on affected modules, sanity testing minimizes unnecessary testing efforts and helps maintain release timelines. Additionally, it supports continuous delivery pipelines by ensuring that builds remain stable before deployment.

Modern tools such as Selenium, Postman, Cypress, Jenkins, and TestNG are commonly used to automate or assist sanity testing workflows. These tools help teams quickly validate UI components, APIs, and backend services after minor changes.

Overall, sanity testing acts as a safety checkpoint in the development process. By quickly validating recent updates and identifying potential risks early, teams can deliver reliable software faster and with greater confidence. Integrating sanity testing into the development workflow ultimately improves software quality, reduces debugging costs, and enhances the overall user experience.

Large Language Models (LLMs) have significantly transformed the way developers write, debug, and maintain software in 2026. What once started as simple autocomplete suggestions has now evolved into intelligent AI-powered coding assistants capable of understanding complex codebases, generating production-ready code, and helping developers solve challenging programming problems faster. These advanced coding LLMs are becoming an essential part of modern development workflows, improving productivity, reducing errors, and accelerating software delivery.

Modern coding LLMs can assist developers in multiple ways, including generating code snippets, debugging errors, explaining unfamiliar code, refactoring legacy systems, and even creating technical documentation automatically. With the ability to understand natural language prompts, developers can now describe what they want to build, and AI models can generate structured, clean, and optimized code across multiple programming languages. This makes LLMs especially useful for startups, enterprise teams, and individual developers looking to increase efficiency and reduce development time.

Choosing the best LLM for coding in 2026 depends on several important factors such as accuracy, context window size, supported programming languages, integration with development tools, pricing, and privacy requirements. Proprietary models like GPT-5, Claude, and Gemini are known for their strong reasoning abilities, large context windows, and enterprise-grade integrations. These models often deliver highly accurate results and are widely used by professional development teams.

On the other hand, open-source alternatives such as DeepSeek-Coder, Code Llama, StarCoder, and Mistral Codestral are gaining popularity due to their flexibility, cost-effectiveness, and self-hosting capabilities. These models allow developers to maintain privacy, customize workflows, and avoid vendor lock-in.

As AI continues to evolve, coding LLMs are becoming powerful AI pair programmers that help developers build better software faster. This guide explores the best LLMs for coding in 2025 and helps developers choose the right AI coding assistant based on their specific needs and development workflows.

If you're building your startup's content engine, this is one of those things that seems minor until it bites you.

Your writer states a fact without a citation. Seems fine. But depending on the context, the audience, and the platform, that fact might need a source. And if a plagiarism checker flags it, your team wastes time rewriting content that could've been handled correctly the first time.

We wrote a clear guide covering what counts as common knowledge, what doesn't, and how to make the right call when it's not obvious.

Full guide: common-knowledge-vs-plagiarism

Free online plagiarism remover: Plagiarism Changer

I’m looking to connect with more developers and tech enthusiasts on daily.dev. It’s been my go-to spot for staying updated with the latest in tech, and I’d love to have more of this community over there. If you want to keep up with the latest trends, share insights, and see what’s bubbling up in the dev world, come join the squad!

👉 Join here: https://dly.to/x1bQADnpLC7

Let’s keep learning and building together! 👨💻✨

Chinese video surveillance companies will be barred from selling internet-connected CCTV cameras and other video surveillance products from April 1, when new certifying rules come into effect.

Chinese brands had ~33% share till 2025; domestic firms now hold >80% share. Certification denial covers products using Chinese chipsets, shifting supply chains.

Mistral AI has released Voxtral TTS, a high-performance, 4-billion parameter open-weights text-to-speech model that competes directly with proprietary tools like ElevenLabs.

It runs on 3 GB of RAM locally and is free. It supports nine languages, offers 3-second voice cloning with high similarity, and delivers sub-second, low-latency performance suitable for on-device applications.

Key Features of Voxtral TTS:

• Performance: Achieved high win rates in human evaluation against top competitors, with superior speaker similarity.
• Efficiency: The 4B model is lightweight enough to run on consumer hardware (laptops, GPUs).
• Voice Cloning: Requires only 3-5 seconds of reference audio for voice cloning and supports cross-lingual voice adoption.
• Capabilities: Generates highly emotive, expressive, and natural-sounding speech across nine languages including English, German, Spanish, and Hindi.
• License: Released under an open-source, permissive license (Apache 2.0), making it available for developers to deploy freely.

This release is part of Mistral's strategy to move into audio and provide open-source alternatives to premium voice AI services.

Source: Mistral