Redbelly Network Troubleshooting Guide Released for Testnet Developers

·3 views

A developer-focused troubleshooting reference for Redbelly Network has been published, compiled in August 2026 from the official Redbelly Developer Portal documentation. The guide covers five key areas including network endpoints, identity-based access, gas fees, contract deployment, and eligibility checks. Unlike standard EVM chains, Redbelly prices gas in USD using an on-chain price oracle, meaning the RBNT amount charged per transaction fluctuates with market conditions. Network access also requires an identity credential from an accredited issuer before wallet-based transactions are permitted, a distinction that often catches developers off guard. The guide also notes that Redbelly's DevNet (chain ID 152) has been formally deprecated, with all contract testing now directed to the Testnet environment.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

AI Hallucinations Persist Despite Model Improvements, Posing Real-World Risks

Despite repeated claims of reduced hallucinations with each new AI model release, large language models continue to fabricate citations, statistics, and even people with unwavering confidence. The core issue lies in how these models work: they predict plausible-sounding text rather than retrieving verified facts, making falsehoods and truths indistinguishable in both tone and fluency. Hallucinations are most frequent in obscure or niche areas — precisely where users rely on AI most and are least able to spot errors. The models show no hesitation or hedging when fabricating, unlike human experts who signal uncertainty at the limits of their knowledge. This has led to documented real-world harm, including lawyers being sanctioned for submitting AI-generated court briefs citing cases that never existed.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why AI Startups All Look, Think, and Fail the Same Way

A wave of AI startups has converged on nearly identical branding, architecture, and business strategies, largely because they are all built as thin layers on top of the same few foundation models. Since the underlying technology is a shared commodity, companies compete on visual design rather than technical differentiation, producing a sea of look-alike landing pages. Most are backed by the same venture capital pools, chasing the same enterprise customers under the same growth-first, monetise-later playbook. This structural uniformity creates a systemic fragility: a single shift in model pricing, a native feature launch by a provider, or a dip in investor sentiment can hit the entire cohort simultaneously. The visual monoculture visible on startup websites is, the argument goes, merely a symptom of a deeper and more dangerous strategic one.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why AI Benchmark Scores Often Fail to Reflect Real-World Performance

AI model launches routinely feature benchmark charts showing performance gains over rivals, yet users frequently find the new models no better — or even worse — for their actual tasks. A core issue is data contamination: because popular benchmarks are publicly available online, models may effectively memorize answers during training, inflating scores without reflecting genuine capability. There is also a commercial incentive at play, as high benchmark results serve as marketing assets, leading vendors to selectively highlight favorable numbers and downplay poor ones. The dynamic illustrates Goodhart's Law — once a metric becomes a target, it loses value as a true measure, with engineering effort funneled toward boosting specific scores rather than broad usefulness. Additionally, benchmark tasks tend to be narrow and auto-gradable, bearing little resemblance to the ambiguous, context-dependent work users actually need AI to perform.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

AI Memory Features Trade User Convenience for Expanding Personal Data Profiles

AI assistant memory features, marketed as a convenience tool, are raising significant privacy concerns as they continuously build detailed personal records from user interactions. Every preference, habit, or candid disclosure shared with an AI is stored and used to infer broader conclusions about a user's health, politics, mood, and finances — often beyond what users knowingly shared. Over time, these accumulated profiles begin shaping the responses users receive, creating a personalization loop that narrows their exposure to information, similar to how social media recommendation algorithms reinforced user biases. The opacity of these systems compounds the problem, as users can rarely inspect the full extent of what the AI has concluded about them from months of conversations. Critics argue that the "memory" toggle, widely adopted without scrutiny, effectively converts candid, low-stakes interactions into a growing dossier that quietly steers the user's information environment.

0 comments Read more at DEV Community