Go Developer Builds Ethereum Wallet CLI to Demystify Crypto Key Management

·1 views

A software developer built a command-line Ethereum wallet in Go to understand the real mechanics behind cryptocurrency wallets, documenting key findings in a technical post on DEV Community. The project revealed that wallets are less about storing keys and more about a series of precise cryptographic decisions, where small implementation errors can break security or compatibility. Following the BIP39 standard, the wallet generates a 12-word mnemonic by combining 128 bits of random entropy with a 4-bit SHA-256 checksum, split into 11-bit groups mapped to a 2,048-word list. The mnemonic then seeds a hierarchical deterministic wallet tree via BIP32, allowing thousands of keys to be derived from a single master key, with hardened derivation requiring a parent private key rather than just a public key. The developer emphasized the project is intended for learning purposes only and not for production use.

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)

American Medical Association Formally Classifies Nearsightedness as a Disease

The American Medical Association (AMA) has endorsed the formal classification of myopia, commonly known as nearsightedness, as a disease. This decision marks a significant shift in how the condition is medically recognized in the United States. The reclassification was announced through the American Academy of Ophthalmology. Such a change could have implications for how myopia is treated, researched, and covered by insurance going forward.

0 comments Read more at Hacker News

ProgrammingDEV Community ·

How Semantic Search Can Be Added to Any App in Three Simple Steps

A developer tutorial published on DEV Community explains how to upgrade a basic keyword-based search into AI-powered semantic search using just three core changes. The approach involves adding a vector column to a database table, generating embeddings via an OpenAI model when a record is created, and comparing vector distances at query time. The tutorial uses a Next.js todo app connected to PostgreSQL as a practical example, demonstrating how naive substring search fails to surface conceptually related results. The matching itself is handled by pgvector, a PostgreSQL extension that enables vector storage and distance-based querying — meaning no model inference occurs at search time. Both the original and upgraded versions of the app are available on GitHub, requiring only a pgvector-enabled Postgres instance and an OpenAI API key to run.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

NocoBase Bug: Filtered Bulk Updates Ignore Conditions, Risk Data Corruption

A code-level investigation into NocoBase 2.2.7 has revealed that bulk update operations do not carry filter conditions into the final SQL UPDATE statement. Instead, the platform first selects matching rows using the filter, then updates those rows by primary key alone — meaning rows that no longer meet the original condition at write time still get updated. The flaw was first reported on the official NocoBase forum on September 4th, with staff acknowledging they were investigating but no fix issued since. Testing on PostgreSQL 16 confirmed the behavior, with SQL logs showing the WHERE clause in the UPDATE containing only primary keys rather than the original filter. The vulnerability is rooted in the repository.ts update method, affects both available code paths, and poses a concurrency risk where stale or changed rows can be incorrectly overwritten.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Vibe Coding Is Fine for Fun, But a Risk in High-Stakes Software

A software engineer with ten years of experience argues that vibe coding — using AI to generate code without reviewing or understanding it — is acceptable for personal projects but dangerous when applied to systems handling sensitive data or financial transactions. The author distinguishes vibe coding from AI-assisted development, where the engineer still reads, reviews, and interrogates the generated code. The piece contends that labeling prompt-only, no-review development as 'engineering' is misleading, since engineering involves understanding, debugging, and accountability. The author warns that deploying unreviewed AI-generated code in production environments risks user data and security, and reflects a lack of curiosity about how software actually works. Until AI tools are reliable enough to work unsupervised, the author urges developers to stay in control and treat AI output with critical scrutiny.

0 comments Read more at DEV Community