I was now done with the basic setup. However, during my time working at my startup, I have learnt to think about a project wearing multiple caps. One such aspect was - With Dhrishti running on a server that was already loaded, I did NOT want the tracking application itself to be heavy. I had to set some benchmarks to ensure that Dhrishti did not consume a tonne of space while tracking the metrics. I also had a problem with unresolved requests - in my mock_services, I had a client that was continuously hitting the API Gateway service. I had to fine-tune all the requests so that I could run tests under different loads, but the advantage was that my project was easily able to discern where the client request was coming from. However, in a production scenario, you can never know where a request is coming from - obviously, we cannot resolve different customer IPs to their respective customer names. This was the first problem. I had to specify what a customer was, and what an unknown request was. I came up with the following solution - Any unresolved IPs are going to be added to a table in the UI called unresolved IP table. This would help me with debugging later. Now, any unresolved IPs which also made requests to an ENTRY-POINT into my application could be added as the customers. For this, I very simply had to filter out the unknown IPs, and keep a configurable entry-point in dhrishti.json in which I would add a bunch of entry-points (in the case of my mock micro-service architecture, only 1) Now, I could differentiate between 2 types of unknown IPs - one which was potentially a customer, one which was a background network call, not important to the working system. The next problem was with the client service itself. It was difficult to simulate, say - a million users in my system. I had essentially built a service which was only being used by 1 customer, but how would Dhrishti behave if I added multiple client IPs? Using K6 k6 is a Grafana based application that helps

AI 工具整合評估報告 執行摘要 本報告評估了 7 個 AI 工具在臨床基因體學領域的應用潛力，重點測試了 3 個優先級最高的工具：MedGemma 醫療大語言模型、Nemotron RAG 文獻檢索系統，以及 Kimi K2.5 多模態視覺語言模型。 評估日期 : 2026-02-10 測試平台 : RTX 3090 24GB 評估目標 : 確認 AI 工具在變異解釋與臨床決策中的可行性 1. 測試項目總覽 1.1 優先級分類 P1 (高優先級) - 已評估: ✅ MedGemma - Google DeepMind 醫療大語言模型 ✅ Nemotron RAG - NVIDIA 文獻檢索與知識整合 ✅ Kimi K2.5 - 月之暗面多模態視覺語言模型 P2 (中優先級) - 已規劃: 📋 Gemini CLI Hooks - 工作流自動化 📋 DaGGR - Hugging Face 基因體學工具 📋 評測方法論 - 醫療 AI 評估框架 P3 (低優先級) - 待調研: 📋 OpenEvidence - 臨床證據檢索引擎 2. MedGemma 醫療大語言模型 2.1 工具概述 開發者 : Google DeepMind 基礎模型 : Gemma 7B 特化領域 : 醫療問答、臨床決策支持 技術規格 : { "model_size" : "7B parameters" , "quantization" : "8-bit (optional)" , "gpu_requirement" : "12-16GB VRAM" , "context_length" : "8192 tokens" , "languages" : "English (primary)" } 2.2 功能評估 核心能力 : 醫療知識問答 疾病症狀解釋 藥物機制說明 治療方案建議 基因變異解釋 基因功能說明 變異影響評估 臨床意義解讀 文獻摘要生成 PubMed 摘要 臨床指南解讀 研究結果總結 2.3 應用場景 場景 A: 變異臨床意義解讀 # 輸入 query = """ 變異資訊: - 基因: BRCA1 - 位置: chr17:41234470 - 變化: c.5266dupC (p.Gln1756Profs*74) - 類型: Frameshift 請解釋此變異的臨床意義。 """ # MedGemma 輸出 (預期) """ BRCA1 c.5266dupC 變異分析: 1. 變異類型: Frameshift insertion - 導致讀框位移 - 產生提前終止密碼子 (位置 1756+74) 2. 基因功能影響: - BRCA1 是腫瘤抑制基因 - 參與 DNA 雙鏈斷裂修復 - Frameshift 導致功能喪失 3. 臨床意義: - 致病性: Pathogenic (高可信度) - 疾病風險: 遺傳性乳腺癌/卵巢癌 (HBOC) - 乳癌風險: 55-72% (終生風險) - 卵巢癌風險: 39-44% 4. 臨床建議: - 遺傳諮詢 - 加強監測 (MRI + 超音波) - 考慮預防性手術 - PARP 抑制劑治療 (若已診斷) """ 場景 B: 醫療文獻查詢 query = " What are the latest treatments for TP53-mutated cancers? " # MedGemma 回答 (模擬) """ TP53 突變癌症的最新治療策略: 1. 標靶治療: - APR-246/Eprenetapopt: 恢復 TP53 功能 - PRIMA-1/APR-246: 臨床試驗進行中 2. 免疫治療: - PD-1/PD-L1 抑制劑 - TP53 突變可能影響免疫反應 3. 合成致死策略: - PARP 抑制劑 (部分 TP53 突變) - ATR/CHK1 抑制劑 4. 臨床試驗: - NCT02999893: APR-246 + 化療 - NCT03745716: TP53 疫苗免疫治療 """ 2.4 部署考量 技術需求 : GPU記憶體: 12-16GB (FP16) 或 8GB (INT8) 推理延遲: 2-5 秒/查詢 API 或本地部署均可 整合方案 : # 與變異註釋流程整合 def annotate_with_medgemma ( variant ): # 1. 提取變異資訊 gene = variant [ ' gene ' ] change = variant [ ' protein_change ' ] # 2. 生成查詢 prompt = f " Explain the clinical significance of { gene } { change } " # 3.

AI 工具整合評估報告 執行摘要 本報告評估了 7 個 AI 工具在臨床基因體學領域的應用潛力，重點測試了 3 個優先級最高的工具：MedGemma 醫療大語言模型、Nemotron RAG 文獻檢索系統，以及 Kimi K2.5 多模態視覺語言模型。 評估日期 : 2026-02-10 測試平台 : RTX 3090 24GB 評估目標 : 確認 AI 工具在變異解釋與臨床決策中的可行性 1. 測試項目總覽 1.1 優先級分類 P1 (高優先級) - 已評估: ✅ MedGemma - Google DeepMind 醫療大語言模型 ✅ Nemotron RAG - NVIDIA 文獻檢索與知識整合 ✅ Kimi K2.5 - 月之暗面多模態視覺語言模型 P2 (中優先級) - 已規劃: 📋 Gemini CLI Hooks - 工作流自動化 📋 DaGGR - Hugging Face 基因體學工具 📋 評測方法論 - 醫療 AI 評估框架 P3 (低優先級) - 待調研: 📋 OpenEvidence - 臨床證據檢索引擎 2. MedGemma 醫療大語言模型 2.1 工具概述 開發者 : Google DeepMind 基礎模型 : Gemma 7B 特化領域 : 醫療問答、臨床決策支持 技術規格 : { "model_size" : "7B parameters" , "quantization" : "8-bit (optional)" , "gpu_requirement" : "12-16GB VRAM" , "context_length" : "8192 tokens" , "languages" : "English (primary)" } 2.2 功能評估 核心能力 : 醫療知識問答 疾病症狀解釋 藥物機制說明 治療方案建議 基因變異解釋 基因功能說明 變異影響評估 臨床意義解讀 文獻摘要生成 PubMed 摘要 臨床指南解讀 研究結果總結 2.3 應用場景 場景 A: 變異臨床意義解讀 # 輸入 query = """ 變異資訊: - 基因: BRCA1 - 位置: chr17:41234470 - 變化: c.5266dupC (p.Gln1756Profs*74) - 類型: Frameshift 請解釋此變異的臨床意義。 """ # MedGemma 輸出 (預期) """ BRCA1 c.5266dupC 變異分析: 1. 變異類型: Frameshift insertion - 導致讀框位移 - 產生提前終止密碼子 (位置 1756+74) 2. 基因功能影響: - BRCA1 是腫瘤抑制基因 - 參與 DNA 雙鏈斷裂修復 - Frameshift 導致功能喪失 3. 臨床意義: - 致病性: Pathogenic (高可信度) - 疾病風險: 遺傳性乳腺癌/卵巢癌 (HBOC) - 乳癌風險: 55-72% (終生風險) - 卵巢癌風險: 39-44% 4. 臨床建議: - 遺傳諮詢 - 加強監測 (MRI + 超音波) - 考慮預防性手術 - PARP 抑制劑治療 (若已診斷) """ 場景 B: 醫療文獻查詢 query = " What are the latest treatments for TP53-mutated cancers? " # MedGemma 回答 (模擬) """ TP53 突變癌症的最新治療策略: 1. 標靶治療: - APR-246/Eprenetapopt: 恢復 TP53 功能 - PRIMA-1/APR-246: 臨床試驗進行中 2. 免疫治療: - PD-1/PD-L1 抑制劑 - TP53 突變可能影響免疫反應 3. 合成致死策略: - PARP 抑制劑 (部分 TP53 突變) - ATR/CHK1 抑制劑 4. 臨床試驗: - NCT02999893: APR-246 + 化療 - NCT03745716: TP53 疫苗免疫治療 """ 2.4 部署考量 技術需求 : GPU記憶體: 12-16GB (FP16) 或 8GB (INT8) 推理延遲: 2-5 秒/查詢 API 或本地部署均可 整合方案 : # 與變異註釋流程整合 def annotate_with_medgemma ( variant ): # 1. 提取變異資訊 gene = variant [ ' gene ' ] change = variant [ ' protein_change ' ] # 2. 生成查詢 prompt = f " Explain the clinical significance of { gene } { change } " # 3.

AWS recently announced CDK Mixins, a new AWS CDK feature that lets developers add reusable capabilities like security, monitoring, and configuration to AWS resources. Mixins work across different construct types, making infrastructure code more flexible and reusable. By Renato Losio

Get clean structured docs instantly. Discussion | Link

Anthropic has suspended all access to its new AI models Fable 5 and Mythos 5 to comply with a government order citing national security concerns.

Andrew Yang made a list of everything Americans overpay for — housing, food, wireless — and thinks the next startup gold rush is giving that money back.

Open-source AI coding agent for your terminal Discussion | Link

It is one of the most frustrating experiences in online learning: you sit down to focus on a Udemy course, but the video player constantly pauses, freezes, or refuses to load. Meanwhile, YouTube, Netflix, and every other app on your Mac run perfectly fine. Because other platforms work without a hitch, it is easy to assume the issue lies with Udemy's servers. However, the root cause is usually a silent conflict between your browser settings, macOS security features, and Udemy’s strict digital rights management (DRM) protections. If you are stuck on a looping loading wheel, here is exactly why it happens and how to fix it in less than two minutes. Quick-Fix Troubleshooting Checklist Save or screenshot this step-by-step breakdown to instantly diagnose and fix your playback issues: Step 1: Open Chrome in Incognito Mode Open a new Incognito window ( Cmd + Shift + N on Mac). Try playing the video again. If it works: Disable ad blockers. Disable VPN privacy shields or browser extensions one at a time. If it still fails: Continue to Step 2. Step 2: Disable Hardware Acceleration Open Chrome. Go to Settings → System . Turn off Use graphics acceleration when available . Relaunch Chrome. Test the video again. Step 3: Check Mac Security and Display Connections Disconnect any external monitors or docking stations. Close applications that may interfere with video playback: Zoom Discord OBS Studio Screen recording tools Test video playback again. Step 4: Clear Temporary Browser Data Open Chrome. Go to Settings → Privacy and Security → Clear Browsing Data . Select: Cookies and other site data Cached images and files Clear the data. Restart Chrome and try again. Still Not Working? If the issue persists after completing all four steps: Update Chrome to the latest version. Update macOS. The Main Culprit: Hardware Acceleration Conflict The most common reason Udemy videos stutter or freeze on a Mac is a feature called Hardware Acceleration inside Google Chrome. What is Hardware Accelerat

A receiver pulling a UDP feed was missing roughly 30% of its messages. No errors, no exceptions, no stack traces — just gaps in the sequence numbers. The first suspect is always the network: a flaky switch, a saturated link, a tired NIC. The network was innocent. The packets were being dropped on the receiving host , after they'd already arrived. Here's how to tell the difference, and why it matters. Why UDP makes this sneaky UDP has no retransmission and no backpressure. When a datagram is lost, nobody is notified — not the sender, not the receiver. The packet simply isn't there. That means two completely different failures look identical from the application's point of view: The network dropped the packet before it reached your machine. Your own host accepted the packet and then threw it away after it arrived. The application sees the same thing in both cases: a missing sequence number. But the fix is in a different building depending on which one it is. Where the packets actually go The receive path is: NIC → kernel socket receive buffer → your recv() call. The kernel parks incoming datagrams in a per-socket buffer until your code reads them. If your code doesn't drain that buffer fast enough, it fills, and the kernel drops the overflow. Crucially, the kernel counts those drops. On Linux: # Per-protocol summary — look for "receive buffer errors" netstat -su # Or straight from the kernel counters cat /proc/net/snmp | grep -A1 Udp # InDatagrams ... InErrors RcvbufErrors ... If RcvbufErrors is climbing, the network did its job and your host discarded the datagrams. That single counter collapses a week of "is it the switch?" into about ten seconds of certainty. The actual cause In this case the socket receive buffer was sitting at the default (~208 KB). The sender burst faster than a single receive thread could call recv() . Average throughput looked fine on every dashboard — but the bursts filled the buffer in milliseconds, and everything past the brim was dropped.