In the right scenarios, it’s already delivering sharper reasoning, smarter reviews, and more accurate design decisions than anything we’ve shipped before. We wanted to get it into your hands immediately so you can explore what’s possible alongside us. It’s early, it’s raw, and we want you to push it. Break it. Tell us where it shines.
You can start using it right away. Open any project in Flux and launch Copilot. Click the model dropdown at the top of the chat panel, select “Next-gen” and then give it a real challenge. Some great starter prompts to see its strengths include:
“Perform a top-to-bottom schematic review for correctness, completeness, and robustness. Assess power, clocks/resets, signal interfaces, analog paths, protection, and passive choices.”
“Replace all low-stock parts with alternatives that meet the same constraints.”
{{try-gpt5}}
The upgrade isn’t just that GPT-5 is a newer model. It brings a different caliber of intelligence to Copilot:
These improvements land harder in Flux because Copilot already has deep, live context on your design—down to parts, pins, nets, properties, constraints, and stackups—so reinforcement models and LLMs can work side-by-side from the canvas up to system architecture. And because Flux is built for agentic workflows—stepwise actions, constraint-aware edits, and iterative design loops right where you work—GPT-5 isn’t starting from scratch; it applies improved reasoning directly to your schematic or layout. Layered on top is a knowledge base of industry best practices and embedded design/process checks, so your AI partner starts from seasoned experience and turns that context into answers that are immediately relevant and actionable.
{{underline}}
In just 48 hours of testing, we saw moments that made us stop and say, “This is new.”
Design a low-noise microphone preamplifier for an electret condenser mic feeding a 24-bit ADC. You must calculate the bias network, gain-setting resistors, coupling capacitors, input high-pass cutoff, output anti-aliasing RC, and decoupling layout. Follow the op-amp and microphone capsule datasheets, ADC input requirements, and industry best practices. It will be integrated into a design. Supply: 3.3V analog rail. Mic bias: 2.0 V through resistor, current ~0.5 mA. Target gain: 20 dB to 40 dB switchable. Bandwidth: 20 Hz to 20 kHz. Input noise target: as low as practical. Include pop-suppression considerations and star-grounding strategy.
{{copy-the-prompt}}
In this case Flux took a plain-English prompt and produced a full low-noise mic preamp to a 24-bit ADC—calculating the right bias, gain, and filter values, choosing real parts, then placing and wiring the entire block with decoupling, VCM bias, and star-ground best practices. It even audited itself (fixed missed ties, made gain legs switchable). The result is a ready-to-review schematic 80% away from layout built end-to-end—complex, competent, and fast.
{{underline}}
{{underline}}
Right now GPT-5 powers Copilot’s chat, but this is just the beginning. We’re already working on:
Open Flux now, switch Copilot to “Next-gen” and see how it handles your next design challenge. The sooner you try it, the more your feedback can shape the next leap in AI-powered hardware design.
{{try-gpt5}}
In the right scenarios, it’s already delivering sharper reasoning, smarter reviews, and more accurate design decisions than anything we’ve shipped before. We wanted to get it into your hands immediately so you can explore what’s possible alongside us. It’s early, it’s raw, and we want you to push it. Break it. Tell us where it shines.
You can start using it right away. Open any project in Flux and launch Copilot. Click the model dropdown at the top of the chat panel, select “Next-gen” and then give it a real challenge. Some great starter prompts to see its strengths include:
“Perform a top-to-bottom schematic review for correctness, completeness, and robustness. Assess power, clocks/resets, signal interfaces, analog paths, protection, and passive choices.”
“Replace all low-stock parts with alternatives that meet the same constraints.”
{{try-gpt5}}
The upgrade isn’t just that GPT-5 is a newer model. It brings a different caliber of intelligence to Copilot:
These improvements land harder in Flux because Copilot already has deep, live context on your design—down to parts, pins, nets, properties, constraints, and stackups—so reinforcement models and LLMs can work side-by-side from the canvas up to system architecture. And because Flux is built for agentic workflows—stepwise actions, constraint-aware edits, and iterative design loops right where you work—GPT-5 isn’t starting from scratch; it applies improved reasoning directly to your schematic or layout. Layered on top is a knowledge base of industry best practices and embedded design/process checks, so your AI partner starts from seasoned experience and turns that context into answers that are immediately relevant and actionable.
{{underline}}
In just 48 hours of testing, we saw moments that made us stop and say, “This is new.”
Design a low-noise microphone preamplifier for an electret condenser mic feeding a 24-bit ADC. You must calculate the bias network, gain-setting resistors, coupling capacitors, input high-pass cutoff, output anti-aliasing RC, and decoupling layout. Follow the op-amp and microphone capsule datasheets, ADC input requirements, and industry best practices. It will be integrated into a design. Supply: 3.3V analog rail. Mic bias: 2.0 V through resistor, current ~0.5 mA. Target gain: 20 dB to 40 dB switchable. Bandwidth: 20 Hz to 20 kHz. Input noise target: as low as practical. Include pop-suppression considerations and star-grounding strategy.
{{copy-the-prompt}}
In this case Flux took a plain-English prompt and produced a full low-noise mic preamp to a 24-bit ADC—calculating the right bias, gain, and filter values, choosing real parts, then placing and wiring the entire block with decoupling, VCM bias, and star-ground best practices. It even audited itself (fixed missed ties, made gain legs switchable). The result is a ready-to-review schematic 80% away from layout built end-to-end—complex, competent, and fast.
{{underline}}
{{underline}}
Right now GPT-5 powers Copilot’s chat, but this is just the beginning. We’re already working on:
Open Flux now, switch Copilot to “Next-gen” and see how it handles your next design challenge. The sooner you try it, the more your feedback can shape the next leap in AI-powered hardware design.
{{try-gpt5}}
Today, we're thrilled to unveil Copilot Experts – a new suite of specialized AI models, each fine-tuned for specific tasks to deliver sharper accuracy and faster performance. Now, you have the power to easily select the AI model that best suits your current task, ensuring more precise and swift responses.
Today, we’re excited to introduce AI Auto-Layout, powered by Flux Copilot. This is the next step toward full layout automation. With just one click, Copilot tackles the repetitive task of routing your board, delivering clean, human-like results that are easy to work with and iterate on.
This post will give you a deeper understanding of how Flux Copilot works, how large language models (LLMs) and agentic systems operate under the hood, and why grounding them in engineering context matters.
Today, we’re excited to share our Summer Update to Flux AI Auto‑Layout, a collection of improvements designed to make one‑click PCB routing more reliable, transparent, and adaptable to your real‑world workflows.
For months, we’ve been adding new features and functions to Copilot to help make it the most powerful AI for hardware design in the world. Today, we’re announcing what might be the biggest upgrade yet: Copilot Vision.
Imagine a future where your most complex PCB design challenges are met with an intelligent AI assistant, capable of handling everything from component selection to compliance checks. Read on to discover how Copilot, embedded within the Flux platform, is turning this vision into a reality, liberating electrical engineers to focus on what truly matters: innovation.
Testing plays a major role in minimizing errors during mass production, yet creating thorough test plans can be challenging and time-consuming.. That’s why we're excited to introduce AI-generated test plans and collaborative workflows, ensuring your hardware is manufactured error-free!
This guide shows how to collaborate with Flux at the schematic stage. You’ll learn how to describe your intent clearly, guide the AI through design decisions, and review results so each iteration gets smarter.
We wanted to bring the power of Python directly into our users' hands so that you can augment your workflows with custom scripts that automate your design and analysis tasks. That’s why, today we’re excited to be introducing Copilot’s Code Interpreter.
This update brings more than just polish—it’s the foundation for a faster, more fluid design experience, built around the way Copilot is used today and the way we see it evolving tomorrow.
Design review is one of the most time-consuming and expensive parts of the hardware design process. Most engineers spend over 30% of their time performing design reviews. What if we could reduce that so that products could ship 30% faster and 30% cheaper?
Today, we’re thrilled to launch a powerful new feature that allows you to declare project requirements like operating temperature, voltage, or compliance standards so Copilot can leverage that knowledge to accelerate tedious tasks like BOM verification, debugging, and part recommendations freeing you to do more of the work you love.
