Notice on Lifting Frontend Transfer Restrictions for Bitlight Wallet Bitlight Wallet will undergo scheduled maintenance on Sep 26 from 10:00 to 11:00 UTC. Upon completion, the frontend transfer restrictions will be lifted, enabling unrestricted transfers of RGB assets via the Bitlight Wallet frontend. A snapshot of all $RGB assets has been taken.

The SEC just gave DeFi frontends a meaningful signal: if you're not custodying assets or executing orders, we won't come after you. Most detailed safe harbor yet. But it's not a rule, it expires in 5 years, next administration can pull it, etc. More substance than we’ve seen in a staff statement. Still not a rule. Still expires. But it’s solid progress in the right direction.

For everyone asking about GPT-5.6's frontend abilities - it still sucks. Nothing is certain in life, except death, taxes, and GPT models generating the sloppiest UIs of all time. Gemini 3.2 Pro is heading in the same direction too, regressing versus 3.1 Pro.

New on the Anthropic Engineering Blog: How we use a multi-agent harness to push Claude further in frontend design and long-running autonomous software engineering. Read more:

i had the idea for TTT last thursday. only one week from idea -> contracts -> frontend -> sell out. i think the most valuable part of TokenWorks is how fast we can ship a novel idea that we think is great

We’re looking for builders to join us as we write the next chapter in prediction market history. The first fully remote roles are now live, with more to come. 🔮 Staff Frontend Engineer 🔮 Principal Backend Engineer 🔮 AI Operator Full details here:

Claude Opus 4.7 is over-trained on the Anthropic website. Every HTML page it designs has that unmistakable Anthropic flavor. GPT-5.5 is still weirdly weak at frontend. It designs frontend like it learned CSS from a backend engineer. OpenAI urgently needs an MTS with taste.

so apparently gemini 3.2 pro is being tested under "gemini-3.1-pro" on @arena's Code Arena (they have done this kind of stealth testing before) ...and if this is really 3.2 pro, it's not looking good. somehow they gpt-ified frontend? hopefully this is an arena-specific quirk

Been thinking about EVM languages and compilers. New languages are set up to fail. ❌You're not competing with Solidity the language. ✅ You're competing with Solidity the stack. There is the backend, IR, frontend, libraries, tooling, docs, education, auditors, trust. 5+ years to build Everyone wants to roll their own compiler because 'yul sucks'. No one has time for rest of the stack. Negative flywheel kicks in. Lot needs to change. Devex needs to improve.

Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research. Frontend development — say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast! Backend development — say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents. Infrastructure. Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development. Research. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally. Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much. I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future posts. [Original text: ]