Welcome to @RoomMining we focus on stabilizing blockchain mining operations, providing solutions for distributed networks. #blockchain #mining #managment #btc

United States
Joined May 2017
Mining ⛏ SHA-256 cryptocurrencies becomes stable at large enough level of scale, as the cost to attack the Proof of Work network (PoW 51% attack) becomes too expensive and unrealistic. This is why #Bitcoin  remains a stable network 10 years later. ⚡️ #btc
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Effective Immediately, all organizations using PHP for their systems are advised to be prepared for an update addressing two critical vulnerabilities. PHP is widely used for web applications. These vulnerabilities often provide a direct path to the server's file system or database. If an immediate update is not feasible, it is recommend that organizations take the system offline until it can be fully mitigated. NSA will follow up shortly with more specific guidance.
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Who's gonna tell vibe coders about... 1. SQL Injection 2. XSS 3. CSRF 4. CORS 5. SSRF 6. IDOR 7. Path Traversal 8. Mass Assignment 9. Clickjacking 10. Content Security Policy 11. Security Headers 12. Input Validation 13. Server-Side Authorization 14. Row-Level Security 15. Least Privilege 16. RBAC 17. Secrets Management 18. .env in .gitignore 19. API Key Rotation 20. Secret Scanning 21. Hashing vs Encryption 22. Password Salting 23. bcrypt & Argon2 24. JWT Expiry 25. Refresh Tokens 26. OAuth 2.0 27. OpenID Connect 28. Session Fixation 29. MFA 30. Brute Force Protection 31. Account Enumeration 32. Timing Attacks 33. File Upload Validation 34. Webhook Signature Verification 35. TLS Certificate Expiry 36. HSTS 37. Encryption at Rest 38. Dependency Vulnerabilities 39. Supply Chain Attacks 40. Threat Modeling 41. Indexes 42. EXPLAIN ANALYZE 43. N+1 Queries 44. Full Table Scans 45. Connection Pooling 46. Database Migrations 47. Down Migrations 48. Transactions 49. ACID 50. Isolation Levels 51. Deadlocks 52. Optimistic Locking 53. Pessimistic Locking 54. Normalization 55. Denormalization 56. Foreign Keys 57. Unique Constraints 58. Cascading Deletes 59. Soft Deletes 60. UUIDs vs Auto-Increment 61. Cursor-Based Pagination 62. Full-Text Search 63. Read Replicas 64. Replication Lag 65. Sharding 66. Partitioning 67. Hot Partitions 68. Postgres VACUUM 69. Materialized Views 70. Backups 71. Testing Your Restores 72. Point-in-Time Recovery 73. Change Data Capture 74. Outbox Pattern 75. ORM Lazy Loading 76. CAP Theorem 77. Eventual Consistency 78. Strong Consistency 79. Raft Consensus 80. Leader Election 81. Split Brain 82. Quorums 83. Clock Skew 84. Distributed Locks 85. Consistent Hashing 86. Bloom Filters 87. Two-Phase Commit 88. CQRS 89. Event Sourcing 90. Race Conditions 91. Backpressure 92. Load Shedding 93. Bulkheads 94. Jitter 95. Retry Storms 96. Cascading Failures 97. Thundering Herd 98. Cache Invalidation 99. Cache Hit Ratio 100. TTLs 101. Cache Warming 102. Write-Through vs Write-Back 103. Redis Eviction Policies 104. CDNs 105. Cache-Control Headers 106. ETags 107. Dead Letter Queues 108. Poison Messages 109. At-Least-Once Delivery 110. Exactly-Once Semantics 111. Message Ordering 112. Kafka Consumer Groups 113. Partition Keys 114. Background Jobs 115. Webhook Retries 116. DNS 117. DNS Propagation 118. HTTP Status Codes 119. HTTP Method Semantics 120. Keep-Alive 121. HTTP/2 122. WebSockets 123. Server-Sent Events 124. Long Polling 125. gRPC 126. Protocol Buffers 127. API Versioning 128. OpenAPI Specs 129. Backward Compatibility 130. Request Size Limits 131. Compression 132. Latency vs Throughput 133. Egress Fees 134. VPCs 135. Subnets 136. NAT Gateways 137. Firewalls 138. Security Groups 139. Service Mesh 140. Ingress Controllers 141. Health Checks 142. Liveness vs Readiness Probes 143. Graceful Shutdown 144. Connection Draining 145. Stateless Services 146. Failover 147. Multi-Region 148. Availability Zones 149. Single Points of Failure 150. Disaster Recovery 151. RTO & RPO 152. SLAs 153. SLOs 154. Error Budgets 155. Incident Response 156. On-Call 157. Runbooks 158. Postmortems 159. Chaos Engineering 160. Graceful Degradation 161. Autoscaling 162. Horizontal vs Vertical Scaling 163. Cold Starts 164. Capacity Planning 165. Load Testing 166. Memory Leaks 167. OOM Killer 168. Structured Logging 169. Log Levels 170. Log Rotation 171. Distributed Tracing 172. OpenTelemetry 173. Correlation IDs 174. Metrics Cardinality 175. p99 Latency 176. Alert Fatigue 177. Error Tracking 178. Profiling 179. Flame Graphs 180. Infrastructure as Code 181. Terraform State 182. GitOps 183. Blue-Green Deployments 184. Canary Releases 185. Rollbacks 186. Feature Flags 187. Environment Parity 188. Staging Environments 189. 12-Factor Apps 190. Multi-Stage Builds 191. Non-Root Containers 192. Image Scanning 193. Resource Requests & Limits 194. Pod Disruption Budgets 195. Semantic Versioning 196. Lockfiles 197. Reproducible Builds 198. Branch Protection
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never join a startup if the founder isn't slightly delusional
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The cryptographic world computer: vitalik.eth.limo/general/202… My attempt to express in somewhat concise terms the true meaning of basically everything planned to happen to Ethereum starting from the fork after Hegota. It's really not just a blockchain anymore. It's a hybrid architecture that combines together blockchains and modern cryptography, to enable much more powerful properties. FOCIL, EIP-8288, Lean consensus, state management, formal verification, advanced mempool improvements (including privacy), and the longer-term specter of obfuscation all mentioned.
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qwen3.8-flash-next ethereum client 100+ GB of local downloaded wikipedia + gutenberg + science articles what else? (Getting rid of the need for centralized pinning services in IPFS is definitely high up the prio list)
I think what's interesting is that the desire for local AI may inadvertently create a lot more local node users. It's a fraction of the actual compute local AI needs and just requires a decent amount of disk space.
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Nvidia has lost it 🤯 China open-sourced an AI that writes CUDA better than humans experts. For years, writing high-performance GPU kernels has been the ultimate dark art in tech. It requires brutal, low-level hardware expertise.. managing memory hierarchies, cache lines, and parallel thread blocks manually. Even top-tier frontier models like Claude and Gemini have historically choked on it, consistently losing to automated compilers like PyTorch. Then ByteDance Seed and Tsinghua University dropped a paper. They built a system called CUDA Agent. Instead of treating code generation as a simple text prompt, they built a large-scale agentic reinforcement learning system. They dropped the AI into a real, sandboxed CUDA development environment complete with automated execution, profiling, and hardware verification tools. The AI was forced to iteratively write code, compile it, run it on physical GPUs, check for errors, and optimize its own execution speed. Over and over again. The results completely shatter current limits. Tested on KernelBench, CUDA Agent didn't just match human engineers. It outperformed the strongest proprietary Western models by roughly 40% on the hardest benchmarks. On complex multi-operator models, it ran up to 92% to 100% faster than traditional compiler optimization (torch.compile). It discovered custom memory access patterns, advanced tiling strategies, and kernel fusions that static compilers couldn't see. For over a decade, human engineers held a monopoly on optimizing hardware performance. That monopoly just ended.
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🤯 it's happening guys... @OpenRouter (just acquired by @Stripe) offers *Sign in with Ethereum* alongside Google and Github @signinethereum
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Super interesting new work from NVIDIA. (bookmark it) They suggest building agents as Python objects. Very cool idea and I think it could a lot with agent reliability. More below: Agent development today spreads across prompt templates, tool schemas, callback code, and workflow graphs. NOOA replaces all four with one abstraction. An agent is a Python object. Its methods are the actions the model can take, its fields hold state, its docstrings are the prompts, and its type annotations act as contracts. A method whose body is "..." gets completed at runtime by a validated LLM loop. A method with a normal body stays deterministic Python. That single convention puts the boundary between probabilistic and deterministic behavior right in the source. Agent behavior becomes testable, traceable, and refactorable with the same tools you already use on the rest of your codebase. NVIDIA reports six model-facing ideas combined on one surface, including pass-by-reference over live objects and model-callable harness APIs for context and events, evaluated on SWE-bench Verified, Terminal-Bench 2.0, and ARC-AGI-3. Paper: arxiv.org/abs/2607.20709 Learn to build effective AI agents in our academy: academy.dair.ai/
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11 years of building, experimenting, questioning and imagining what the internet could become. Ethereum’s future is still being shaped by its community. To celebrate @ethereum's birthday, take 11% off Devcon 8 GA tickets through August 11.
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The Hugging bay, The Pirate bay for open LLM's, model weights downloadable with torrents. This is a game changer. huggingbay.xyz
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A dev got so frustrated watching his AI agent write 500 lines for a 5-line problem that he built a fix. He called it Ponytail. Named after the guy every team has - long ponytail, oval glasses, been there longer than the version control. You show him fifty lines; he looks at them, says nothing, and replaces them with one. Now your agent does the same. Before writing anything, it looks for a reason not to. 80-94% less code. 47-77% cheaper. 3-6x faster. The best code is the code you never wrote. GitHub Repo: github.com/DietrichGebert/po…
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Instead of watching an hour of Netflix, watch this 30-minute speech by the Head of Anthropic’s Coding Agents research team. It will teach you more about vibe coding than 100 paid courses.
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Viktor X'te tasarım tarafında takip ettiğim tartışmasız en iyi isimlerden biri. Adamın çıkardığı işler her seferinde estetikten yıkılıyor. Ve şimdi Claude Design + Opus 4.7 üzerine 18 dakikalık tutorial çıkarmış. Kendi adıma bu videoyu izledikten sonra tasarımlarınızın tamamen bambaşka bir seviyeye çıkacağına eminim. Claude Design zaten tek başına oyunu değiştiriyor, bir de Viktor gibi birinin elinden öğrenmek varken kaçırılmayacak bir kaynak. Yumulun!
Claude Design is insane. ❤️‍🔥Just recorded a 18-min tutorial on how to build animated, award-winning websites with Claude Design + Opus 4.7!
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Here’s to the crazy ones.
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ZKPass is built on Ethereum. @zkPass is a privacy-preserving identity protocol letting users prove information about themselves without revealing the underlying data. It enables secure verification of credentials onchain while keeping personal information private by default.
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Holy shit, this is INSANE. Andrej karpathy just open-sourced an autonomous AI researcher that runs 100 experiments while you sleep > He built a free AI agent that improves itself while you sleep > it runs fully autonomously, executing real experiments with no supervision > runs 100 experiments per night, one every 5 minutes on a fixed budget > edits its own training code, measures validation loss, keeps improvements and discards failures > results automatically adapt to your specific hardware > only one GPU is needed to run it > Best part is it’s completely free.
I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autorese… Part code, part sci-fi, and a pinch of psychosis :)
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Agents can now create email inboxes with USDC on @base AgentMail supports @CoinbaseDev’s x402 protocol to give agents access to email without accounts or API keys See how it works 👇
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Now, the quantum resistance roadmap. Today, four things in Ethereum are quantum-vulnerable: * consensus-layer BLS signatures * data availability (KZG commitments+proofs) * EOA signatures (ECDSA) * Application-layer ZK proofs (KZG or groth16) We can tackle these step by step: ## Consensus-layer signatures Lean consensus includes fully replacing BLS signatures with hash-based signatures (some variant of Winternitz), and using STARKs to do aggregation. Before lean finality, we stand a good chance of getting the Lean available chain. This also involves hash-based signatures, but there are much fewer signatures (eg. 256-1024 per slot), so we do not need STARKs for aggregation. One important thing upstream of this is choosing the hash function. This may be "Ethereum's last hash function", so it's important to choose wisely. Conventional hashes are too slow, and the most aggressive forms of Poseidon have taken hits on their security analysis recently. Likely options are: * Poseidon2 plus extra rounds, potentially non-arithmetic layers (eg. Monolith) mixed in * Poseidon1 (the older version of Poseidon, not vulnerable to any of the recent attacks on Poseidon2, but 2x slower) * BLAKE3 or similar (take the most efficient conventional hash we know) ## Data availability Today, we rely pretty heavily on KZG for erasure coding. We could move to STARKs, but this has two problems: 1. If we want to do 2D DAS, then our current setup for this relies on the "linearity" property of KZG commitments; with STARKs we don't have that. However, our current thinking is that it should be sufficient given our scale targets to just max out 1D DAS (ie. PeerDAS). Ethereum is taking a more conservative posture, it's not trying to be a high-scale data layer for the world. 2. We need proofs that erasure coded blobs are correctly constructed. KZG does this "for free". STARKs can substitute, but a STARK is ... bigger than a blob. So you need recursive starks (though there's also alternative techniques, that have their own tradeoffs). This is okay, but the logistics of this get harder if you want to support distributed blob selection. Summary: it's manageable, but there's a lot of engineering work to do. ## EOA signatures Here, the answer is clear: we add native AA (see eips.ethereum.org/EIPS/eip-8… ), so that we get first-class accounts that can use any signature algorithm. However, to make this work, we also need quantum-resistant signature algorithms to actually be viable. ECDSA signature verification costs 3000 gas. Quantum-resistant signatures are ... much much larger and heavier to verify. We know of quantum-resistant hash-based signatures that are in the ~200k gas range to verify. We also know of lattice-based quantum-resistant signatures. Today, these are extremely inefficient to verify. However, there is work on vectorized math precompiles, that let you perform operations (+, *, %, dot product, also NTT / butterfly permutations) that are at the core of lattice math, and also STARKs. This could greatly reduce the gas cost of lattice-based signatures to a similar range, and potentially go even lower. The long-term fix is protocol-layer recursive signature and proof aggregation, which could reduce these gas overheads to near-zero. ## Proofs Today, a ZK-SNARK costs ~300-500k gas. A quantum-resistant STARK is more like 10m gas. The latter is unacceptable for privacy protocols, L2s, and other users of proofs. The solution again is protocol-layer recursive signature and proof aggregation. So let's talk about what this is. In EIP-8141, transactions have the ability to include a "validation frame", during which signature verifications and similar operations are supposed to happen. Validation frames cannot access the outside world, they can only look at their calldata and return a value, and nothing else can look at their calldata. This is designed so that it's possible to replace any validation frame (and its calldata) with a STARK that verifies it (potentially a single STARK for all the validation frames in a block). This way, a block could "contain" a thousand validation frames, each of which contains either a 3 kB signature or even a 256 kB proof, but that 3-256 MB (and the computation needed to verify it) would never come onchain. Instead, it would all get replaced by a proof verifying that the computation is correct. Potentially, this proving does not even need to be done by the block builder. Instead, I envision that it happens at mempool layer: every 500ms, each node could pass along the new valid transactions that it has seen, along with a proof verifying that they are all valid (including having validation frames that match their stated effects). The overhead is static: only one proof per 500ms. Here's a post where I talk about this: ethresear.ch/t/recursive-sta… firefly.social/post/farcaste…
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[BREAKING] AI agents can now use crypto fully autonomously. Pay, trade, build apps. In 1 prompt. No human required.
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