You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
Greetings loafers! (λ-gophers haha, get it?) let-go is a Clojure dialect with a bytecode compiler and stack VM, written in Go. A single ~10.7MB binary, ~7ms cold start, no JVM. It passes the jank-lang test suite. I started this in 2021 as an elaborate joke: an excuse to write Clojure while pretending to write Go. It turned out useful. I use it for CLIs, scripts, and web servers, and I built a daem
ripgrep is faster than {grep, ag, git grep, ucg, pt, sift} In this article I will introduce a new command line search tool, ripgrep, that combines the usability of The Silver Searcher (an ack clone) with the raw performance of GNU grep. ripgrep is fast, cross platform (with binaries available for Linux, Mac and Windows) and written in Rust. ripgrep is available on Github. We will attempt to do the
Which programming languages are most token-efficient? I've been trying to think through what happens to programming languages and tooling if humans are increasingly no longer writing it. I wrote about how good agents are at porting code recently, and it got me thinking a bit more about what constraints LLMs have vs humans. One of the biggest constraints LLMs have is on context length. This is a di
ãç²ãæ§ã§ãã ç¾å¨ãéè¦ãªçµå¶æ¡ä»¶ã®å¯¾å¿ä¸ã«ã¤ãããã®ã¡ã¼ã«ã¯ä»ã®å½¹å¡ã»ååã«ã¯å ±æããªããã ãé¡ããããã¾ãã ä»ãããå¼ç¤¾ã®DMARCã¬ã³ã¼ãã確èªããè¨å®æååãåå¾ãã¦ãã ããã åå¾å¾ã¯ããã®æååãQRã³ã¼ãåãã¦ãè¨äºã³ã¡ã³ãæ¬ã« æ¸ãè¾¼ãã§ããã ãããããé¡ããããã¾ãã çãããçªç¶ç¤¾é·ãããããªã¡ã¼ã«ãæ¥ããã©ããã¾ããï¼ éä¿¡å ã®ã¡ã¼ã«ã¢ãã¬ã¹ã確èªããï¼ ãããï¼ã»ãã¥ãªãã£ã®æè²ãããåãã¦ãã¾ããï¼ ãããã¡ã¼ã«ã¢ãã¬ã¹ãæ£ãããã ã£ããâ¦â¦ï¼ DMARCã reject ã§è¨å®ããã¦ããã°ããããããã®ã¡ã¼ã«ã¯å±ãã¦ããªãããããã¾ããã ãã£ãã ç§ã®ä¼ç¤¾ã§ã¯DMARCããªã·ã¼ã®å¼·åãæ¤è¨ããããã®ã®ãè¦éãã«ãªãã¾ãããããããä»ã®ä¼ç¤¾ã¯ã©ããªããï¼ã ã¨æãã調æ»ãããããããªä¸å ´ä¼æ¥ã«çµã£ã¦DMARCè¨å®ç¶æ³ã調ã¹ã¦ã¿ã¾ããã DMARCã¨ã¯ DM
CVSSï¼Common Vulnerability Scoring Systemï¼ã¯ãèå¼±æ§ç®¡çã«ãããåºæ¬çãªä»çµã¿ã¨ãã¦åºãå©ç¨ããã¦ãããæ¥çå ¨ä½ã®ããã¡ã¯ãã¹ã¿ã³ãã¼ãã«ãªã£ã¦ãã¾ããCVSSã¯FIRSTï¼Forum of Incident Respones and Security Teamsï¼å ã«è¨ç½®ãããCVSS-SIGï¼Special Interest Groupï¼1ã«ãã£ã¦çå®ããã2023å¹´7æç¾å¨ã®ææ°ãã¼ã¸ã§ã³ã¯3.1ã¨ãªã£ã¦ãã¾ãã2023å¹´6æã«æ¬¡ãã¼ã¸ã§ã³ã§ãã4.0ã®ãããªãã¯ãã¬ãã¥ã¼ç2ãå ¬éããã¦ãããå¯ããããã³ã¡ã³ããã¬ãã¥ã¼ã»åæ ããå¾ã2023å¹´10æãç®éã«ãã¼ã¸ã§ã³4.0ã®å ¬éãäºå®ããã¦ãã¾ããæ¬ç¨¿ã§ã¯ãããªãã¯ãã¬ãã¥ã¼çã«åºã¥ãã¦ãç¾è¡ã®ãã¼ã¸ã§ã³3.1ã¨ã®å¤æ´ç¹ã解説ãã¾ããã¾ããSSVCï¼Stakeholder-Specific
2020-03-19 Shodan ã«ã¯ããªã¼ã®ã¢ã«ã¦ã³ãã®ä»ã«ã¡ã³ãã¼ã·ããï¼Shodan Membershipï¼ã¨ãããã®ãããã ããªã¼ã®ã¢ã«ã¦ã³ãã§ãã°ã¤ã³ãã¦ããç¶æ ã§ä¸è¨ã«ã¢ã¯ã»ã¹ããã¨ãã¡ã³ãã¼ã·ããã«ã¤ãã¦ã®èª¬æãé²è¦§ã§ããã Shodan Membership ã¡ã³ãã¼ã·ããã«ã¤ãã¦ã®èª¬æãã¼ã¸ ãã¼ã¸ã®è§£èª¬ãèªãã ãã ã¨ã§ããããã«ãªããã¨ãåããã«ããã£ãã ä»å Shodan ã®ã¡ã³ãã¼ã·ããã¢ã«ã¦ã³ãã使ããã®ã§ã ããªã¼ã®ã¢ã«ã¦ã³ãã¨æ¯ã¹ã¦ã§ããããã«ãªã£ããã¨ãã¾ã¨ãã¦ã¿ãã ã¢ã«ã¦ã³ãã®éãã¾ã¨ã é ç® ããªã¼ ã¡ã³ãã¼ã·ãã ä¾¡æ ¼ ç¡æ $49 æ¤ç´¢çµæ 2ãã¼ã¸ï¼20ä»¶ï¼ 20ãã¼ã¸ï¼200ä»¶ï¼ Download Credits 0 20 Shodan Map å°ãªã å¤ã Shodan Images å©ç¨ä¸å¯ å©ç¨å¯è½ Shodan API 0ï¼ Q
åªå çã«è¦ããå®ã¯äºæ¬¡æ å ±ãå¤ãããã峿æ§ã¯ããã¾ã§é«ããªããã¨ãå¤ããå¤ãã¯1æ¥ããæ°æ¥ã®ã©ã°ãããã䏿¬¡æ å ±ã¨ããã¨"BleepingComputer has learned" ã404 Mediaã®ç¬èªåæç¨åº¦ãã æ£ç¢ºæ§ã«é¢ãã¦ã¯4ã¨ãã¦ããããå ¨ä½çå¾åã¨ãã¦ç¤¾ä¼çè¨äºã«ã¤ãã¦ã®æ£ç¢ºæ§ã¯é«ã䏿¹ãæè¡çè¨äºã«é¢ãã¦ã¯èª¤ã£ãçè§£ããã¨ã«è¨äºåããããã¨ããã°ãã°ããã®ã§ãæªãããªã¨æã£ããå æ å ±æºãè¦ãªããèªãããã«ãã¦ããã ç義ã®ããµã¤ãã¼ã»ãã¥ãªãã£ãã«å¯¾ããç¶²ç¾ æ§ã¯é«ãããå¨è¾ºåéï¼ãµã¤ãã¼æ¿çãGRCããµã¤ãã¼ç¯ç½ª1çï¼ã«å¯¾ããç¶²ç¾ æ§ã¯å®ã¯é«ããªããã¨ã¯ãããã¹ã¿ã¼ãã©ã¤ã³ã¨ãã¦ã¯åªç§ãã¾ãã¯Bleeping Computerããã¨ããããèãçµè«ã«ãªãã ä¸è¬ãã¥ã¼ã¹ãµã¤ãï¼å½éï¼ è¦åºãã¸ã®ãªã³ã¯ å³ææ§ æ£ç¢ºæ§ ç¶²ç¾ æ§ åéæ§
Copilot ã«è³ªåãããããã§ãããã¾ã§ãã¾ã AI ã«ã¯ç©æ¥µçã«ã¯é¢ãã£ã¦ããªãã£ãã®ã§ãããè¦æãããã10æ¥éã»ã©ããã¦åå¼·ã»æ´çãã¦ã¿ã¾ãããããããæè¿ã®æ°åãå¤åãæ¿ããããããã®ãã¼ã¸ã®å 容ãããã«å¤ããªã£ã¦ãã¾ãå¯è½æ§ãããã¾ããææ°ã®åå㯠AI ãæ´»ç¨ãããªã©ãã¦ã¦ã©ãããã¦ã¿ã¦ãã ããã(2025.10.12 æç«ã ) ãµããã¼ã¸ AIã®æ´å² AIé¢é£ç¨èª åºæ¬ç¨èª æ©æ¢°å¦ç¿ ãã£ã¼ãã©ã¼ãã³ã° çæAI AIã¨ã¼ã¸ã§ã³ã ãã¤ãã³ã¼ãã£ã³ã° ã¢ãã«ã¨å ¥åºå ã¢ãã« ããã³ãã ãã¼ã¯ã³ ãã«ãã¢ã¼ãã« MCP ã¢ã¼ããã¯ã㣠ãã©ã¡ã¼ã¿æ° LLM SLM GPU å¦ç¿æ¹æ³ã»æ¸¬å®æ¹æ³ æå¸«ããå¦ç¿ æå¸«ãªãå¦ç¿ ãã¡ã¤ã³ãã¥ã¼ãã³ã° 転移å¦ç¿ RAG AIã®èª²é¡ã¨æªæ¥ AIå«çã¬ã¤ãã©ã¤ã³ã»æ³å¾ ãã«ã·ãã¼ã·ã§ã³ AGI ã·ã³ã®ã¥ã©ãªã㣠AIã¢ãã« ChatGP
1. å§ãã« ããã«ã¡ã¯ãmorioka12 ã§ãã æ¬ç¨¿ã§ã¯ããµã¤ãã¼ã»ãã¥ãªãã£ã®åãçµã¿ã®ä¸ç°ã§ããã°ãã¦ã³ãã£å¶åº¦ã®æ´»ç¨ç¶æ³ã«ã¤ãã¦ç´¹ä»ãã¾ãã ãªããæ¬å 容ã¯ããæè¿ã§ããã°ãã³ã¿ã¼è¦ç¹ã®è©±ã ãã§ãªããããã°ãã¦ã³ãã£ãã©ãããã©ã¼ã ãæ´»ç¨ããäºæ¥è ã®è©±ããã社å ãã°ãã¦ã³ãã£å¶åº¦ãã«ã¤ãã¦èããããã¨ãå¢ãããããèªåãªãã«æ°ã«ãªãç¹ãå°ãæ´çãã¦ã¿ãå 容ã§ãã ç®æ¬¡ 1. å§ãã« æ³å®èªè 2. ãã°ãã¦ã³ãã£ã¨ã¯ ãã°ãã¦ã³ãã£ãã©ãããã©ã¼ã 3. åå½ã®ãã°ãã¦ã³ãã£ç¶æ³ ã¢ã¡ãªã« ðºð¸ 欧å·é£å(EU)ðªðº ãã©ã³ã¹ ð«ð· ã·ã³ã¬ãã¼ã« ð¸ð¬ æ¥æ¬ ð¯ðµ ãã®ä» 4. ãã°ãã¦ã³ãã£ã¨èå¼±æ§è¨ºæã®å®æ½ç¶æ³ ããæ´ç 5. ãã°ãã¦ã³ãã£ã®æè³å¯¾å¹æ ããæ´ç 6. 社å ãã°ãã¦ã³ãã£å¶åº¦ 7. ãã®ä» AI/LLM ã«ããããã°ãã¦ã³ã㣠海å¤ãã°
ç·å1ä½å¸¸é£ ãããã®çº é® ããã 餿²¹æ¼¬ã 200g / 400g / 800g / 1.6kg / 2.4kg 200gããã¯[å 容éãé¸ã¹ã] ãµããã¨ç´ç¨ ããã æµ·é®® åæµ·é ã¤ã¯ã© å°åã ãµãã㨠ã©ã³ãã³ã° äººæ° é«è©ä¾¡ ç½ç³ çº
ãªãªã¼ã¹ãé害æ å ±ãªã©ã®ãµã¼ãã¹ã®ãç¥ãã
ææ°ã®äººæ°ã¨ã³ããªã¼ã®é ä¿¡
å¦çãå®è¡ä¸ã§ã
j次ã®ããã¯ãã¼ã¯
kåã®ããã¯ãã¼ã¯
lãã¨ã§èªã
eã³ã¡ã³ãä¸è¦§ãéã
oãã¼ã¸ãéã
{{#tags}}- {{label}}
{{/tags}}