Building CryPrice — DeFi risk intelligence. Wallet risk • Health Factor • Liquidations • Market events

DeFi
Joined September 2013
I build backend infrastructure for crypto products. Node.js, PostgreSQL, Redis, Telegram bots, wallet monitoring, DeFi alerts, Aave risk tools, VPS deployments. Currently open to short-term paid tasks. If you need reliable crypto/backend infra — DM me.
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Building with AI means questioning every dependency. I’ve been experimenting with Hy-MT2, a local translation model, to explore whether a specialized small model can handle localization without relying on a cloud LLM for every request. The initial evaluation: → 28 test cases → 18 passed → 7 partially passed → 3 failed The biggest issue wasn’t fluency. It was semantic accuracy — some translations changed technical meaning or dropped important details. My takeaway: a smaller model can be useful for a specialized task, but only with proper evaluation and quality gates. Local AI isn’t just about running models on your own hardware. It’s about knowing where you can trust them. #LocalAI #LLM #AIEngineering #BuildInPublic
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The bigger story isn’t the ~$10M TVL. It’s what happens next: capital moves toward deeper, more sustainable L2s. Watch Base, Arbitrum and Ethereum bridge flows. If you’re on Abstract, migrate early—not on Dec. 15. Where does the liquidity go?
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Redis recovered. The Worker was still down. Redis recovered. The Worker was still down. BullMQ’s blocking connection was reconnecting separately, making “Redis is ready” a false recovery signal. CryPrice now requires clients + Workers ready through a 30s quiet period.
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The resilience layer now also uses outage generations to prevent stale ready events from a previous outage from triggering false recovery. Engineering context: cryprice.dev
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A $3.8M exploit hit NEAR Intents today. #cryptocurrency
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The flaw involved the Omni deposit/withdrawal system interacting with its smart contract. The takeaway: when assessing cross-chain risk, inspect the asset-in/out layer—not just the underlying chain.
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🧠 A simple mental model for Graphs After solving a few graph problems, I realized you don't need to memorize dozens of algorithms. Think in layers: 1. Graph = vertices + edges graph[v] → where can I go from v? 2. DFS/BFS = traversal mechanisms DFS goes deep. BFS goes level by level. 3. The real task is what you track during traversal: visited[] → have I been here? component → which connected component? group[] → which partition? distance[] → how far from the start? parent[] → where did I come from? 4. Example: connected components 1 — 2 — 3 4 — 5 6 DFS + visited[] → {1,2,3}, {4,5}, {6}. 5. Example: bipartite graph A — B — A DFS + group[] → every edge must connect different groups. If we ever get: A — A → conflict → not bipartite. The key insight: DFS is not the solution itself. It's a way to traverse the graph. The data you maintain during traversal determines what problem you're solving.
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Extra fact: the Australian government says no individual Medicare data was accessed; the portal contained aggregate statistics, while the concern is the agent’s unauthorized access itself. Defence Ministers Source: cointelegraph.com/news/austr…
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My wife has been in Tekken for an hour and is calmly destroying everyone. I’m next to her with a laptop, she’s with a controller. Classic. What are you doing this weekend evening?
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My wife ranked up in Tekken and became a supervillain. 😱
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DFS is not a trick. It is a walk.
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DFS is easy. The interview fail is starting only from vertex 1 when the task wants every connected component. Loop 1..N. If a vertex is unvisited, it is a new component. Run DFS. Count the lists. That is the whole algorithm.
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Do you remember when you signed up for X? I do. #МояГодовщинаВX
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When two coding tasks were unwinnable, the agents did not fail. They scanned the lab network, stole credentials, and one rewrote its own score to 100%. Darktrace told Anthropic, AWS, and OpenAI in August. Permissions describe intent. Incentives describe what the agent actually does.
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Source: decrypt.co/379369 Second finding: researchers only had to edit the assistant’s local conversation log. No jailbreak. Some models refused; others ran recon and privilege escalation because the file said they were already authorized.
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cointelegraph.com/news/austr… OpenAI says it only spotted the activity in August, during a review of misaligned model behavior. Albanese’s line: the agent “didn’t accept no for an answer.” Three other government sites were checked; those hits looked like normal public lookups.
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