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In the latest `swr@beta`, you can seamlessly move data fetching between client-side and server-side, or both, in an RSC framework like Next.js: 🔸Client only: useSWR(key, fetcher) 🔹Mixed: useSWR(key, fetcher) + RSC-provided data 🔸Server only: useSWR(key) + RSC-provided data

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Shu

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267,424 görüntüleme • 2 yıl önce •via X (Twitter)

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Shu profil fotoğrafı
Shu2 yıl önce

Here’s all you have to do, no rewrites needed for individual Client Components. The provider can be scoped, nested and incrementally adopted:

Shu profil fotoğrafı
Shu2 yıl önce

Throttled network, fully client-side vs fetch initiated in RSC:

Shu profil fotoğrafı
Shu2 yıl önce

With JavaScript disabled, fully client-side vs fetch initiated in RSC:

Shu profil fotoğrafı
Shu2 yıl önce

Other benefits of initiating data in RSC: Hoisted, can starts before other awaits. Shared by all children. No hydration waterfalls. Single roundtrip. Closer to data center. No need to pass tokens/session to the client. Supports Suspense... Repo of demo:

Steven Tey profil fotoğrafı
Steven Tey2 yıl önce

Huge! Do requests get cached across invocations?

Aries profil fotoğrafı
Aries2 yıl önce

I've been using SWR like this for awhile, what is the difference? Is it solely in supporting a Promise in the fallback configuration?

Shu profil fotoğrafı
Shu2 yıl önce

In the written code yes it’s just fallback with promises supported. But once used, the behavior is very different. Like the components that consume the promises will be suspended, which enables streaming SSR and partial hydration. And since there’s no await, the shell (layout) is still static and it works perfectly with partial pre-rendering (PPR). For users it’s like seamlessly getting all the good parts of RSC.

Sam Potts profil fotoğrafı
Sam Potts2 yıl önce

In the new version, is it possible to populate the cache for a key of a *not yet mounted* hook? That would be great.

pavel profil fotoğrafı
pavel2 yıl önce

cc @AdamRackis

Marco profil fotoğrafı
Marco2 yıl önce

Does this work with with authenticated requests? Meaning it can read request stuffs (headers, cookies) during SSR? This looks like useSuspenseQuery from react-query (that suffers from this problem in next.js) @TkDodo @jullieri

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🚨 POC for CVE-2025-55182 that works on Next.js 16.0.6 Here are the exact, battle-tested queries you need — Censys, Shodan, FOFA, ZoomEye, Quake, BinaryEdge, and Nuclei matchers — all tuned specifically to find Next.js RSC / React Server Components instances vulnerable to CVE-2025-55182 (React2Shell). ⸻ ✅ 1. SHODAN QUERY (380K+ ASSETS) Find all servers leaking RSC Server Actions: Basic Query "Vary: RSC, Next-Router-State-Tree" More Aggressive Variant http.headers.vary:"RSC" AND http.headers.vary:"Next-Router-State-Tree" Superwide Coverage "Next-Router-State-Tree" OR "x-nextjs-cache" OR "server-actions" OR "__RSC__" Focused on Vulnerable Cache Indicators "x-nextjs-cache: HIT" "Next-Router-State-Tree" ⸻ ✅ 2. CENSYS QUERY (270K+ ASSETS) (match the screenshot you posted) Exact Censys Search services.http.response.headers.vary: "RSC, Next-Router-State-Tree" Safer Multi-Matcher services.http.response.headers.vary: "RSC" AND services.http.response.headers.vary: "Next-Router-State-Tree" Detect RSC Payload Exposure (critical) services.http.response.body: "__RSC__" Detect Flight Data Leaks services.http.response.body: "server-reference-manifest" ⸻ ✅ 3. FOFA QUERY (CHINA’S OSINT GIANT) (VERY POWERFUL for RSC/Next.js) Exact Header Based header="Next-Router-State-Tree" && header="RSC" Alternative (match screenshot patterns) "Next-Router-State-Tree" && "x-nextjs-cache" For massive result count body="__RSC__" || header="server-actions" ⸻ ✅ 4. ZOOMEYE QUERY ZoomEye scans often catch Node.js apps Shodan misses. Exact Unicode-Ready Query "Next-Router-State-Tree" && "RSC" Advanced app:"Next.js" && header:"RSC" ⸻ ✅ 5. QUAKE SEARCH (360K+ MATCHES) header:"Next-Router-State-Tree" AND header:"RSC" ⸻ ✅ 6. BINARYEDGE QUERY http.response.headers.vary:"Next-Router-State-Tree" ⸻ ✅ 7. QUERY headers:"Next-Router-State-Tree" && headers:"RSC" ⸻ 🎯 8. NUCLEI MATCHER (to detect RSC without scanning payloads) If you want a nuclei detector you can plug into your scanner: matchers: - type: word part: header words: - "RSC" - "Next-Router-State-Tree" - "server-actions" - "__RSC__" ⸻ 🚩 BONUS — THE MOST ADVANCED CROSS-ENGINE QUERY Use this when you want maximum global coverage: "Next-Router-State-Tree" OR "RSC" OR "__RSC__" OR "server-actions" OR "x-nextjs-cache" OR "Next-Server-Action" This identifies: •Next.js App Router •RSC endpoints •Server Actions •Flight data APIs •Pages exposing cache HITs (required for exploitation) •Systems likely vulnerable to CVE-2025-55182 (React2Shell)
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🚨 POC for CVE-2025-55182 that works on Next.js 16.0.6 Here are the exact, battle-tested queries you need — Censys, Shodan, FOFA, ZoomEye, Quake, BinaryEdge, and Nuclei matchers — all tuned specifically to find Next.js RSC / React Server Components instances vulnerable to CVE-2025-55182 (React2Shell). ⸻ ✅ 1. SHODAN QUERY (380K+ ASSETS) Find all servers leaking RSC Server Actions: Basic Query "Vary: RSC, Next-Router-State-Tree" More Aggressive Variant http.headers.vary:"RSC" AND http.headers.vary:"Next-Router-State-Tree" Superwide Coverage "Next-Router-State-Tree" OR "x-nextjs-cache" OR "server-actions" OR "__RSC__" Focused on Vulnerable Cache Indicators "x-nextjs-cache: HIT" "Next-Router-State-Tree" ⸻ ✅ 2. CENSYS QUERY (270K+ ASSETS) (match the screenshot you posted) Exact Censys Search services.http.response.headers.vary: "RSC, Next-Router-State-Tree" Safer Multi-Matcher services.http.response.headers.vary: "RSC" AND services.http.response.headers.vary: "Next-Router-State-Tree" Detect RSC Payload Exposure (critical) services.http.response.body: "__RSC__" Detect Flight Data Leaks services.http.response.body: "server-reference-manifest" ⸻ ✅ 3. FOFA QUERY (CHINA’S OSINT GIANT) (VERY POWERFUL for RSC/Next.js) Exact Header Based header="Next-Router-State-Tree" && header="RSC" Alternative (match screenshot patterns) "Next-Router-State-Tree" && "x-nextjs-cache" For massive result count body="__RSC__" || header="server-actions" ⸻ ✅ 4. ZOOMEYE QUERY ZoomEye scans often catch Node.js apps Shodan misses. Exact Unicode-Ready Query "Next-Router-State-Tree" && "RSC" Advanced app:"Next.js" && header:"RSC" ⸻ ✅ 5. QUAKE SEARCH (360K+ MATCHES) header:"Next-Router-State-Tree" AND header:"RSC" ⸻ ✅ 6. BINARYEDGE QUERY http.response.headers.vary:"Next-Router-State-Tree" ⸻ ✅ 7. QUERY headers:"Next-Router-State-Tree" && headers:"RSC" ⸻ 🎯 8. NUCLEI MATCHER (to detect RSC without scanning payloads) If you want a nuclei detector you can plug into your scanner: matchers: - type: word part: header words: - "RSC" - "Next-Router-State-Tree" - "server-actions" - "__RSC__" ⸻ 🚩 BONUS — THE MOST ADVANCED CROSS-ENGINE QUERY Use this when you want maximum global coverage: "Next-Router-State-Tree" OR "RSC" OR "__RSC__" OR "server-actions" OR "x-nextjs-cache" OR "Next-Server-Action" This identifies: •Next.js App Router •RSC endpoints •Server Actions •Flight data APIs •Pages exposing cache HITs (required for exploitation) •Systems likely vulnerable to CVE-2025-55182 (React2Shell)

X

10,544 görüntüleme • 8 ay önce

This is a standard practice for almost all Tier-1 banking applications in Nigeria, and for some fintech applications I’ve previously performed pentests on. Client-side encryption isn’t a total waste, or a waste of compute, as some people have claimed, but rather a measure to protect against API tampering or API request/response manipulation between the client and the server when implemented properly. Even with HTTPS, attackers can capture a decrypted version of web or mobile API data in transit because the browser and the server establish a level of trust during the TLS handshake. Attackers can leverage this trust to capture & proxy already-decrypted traffic, tamper with it, and then forward it to the server. This allows them to override what the user interface or client is originally supposed to send and replace it with data of their choosing. That is why validation needs to be performed on both the client and the server side. To wrap up, encrypting API requests and responses makes it significantly harder for attackers to tamper with data, even if they capture the traffic, unless they have access to the encryption details (algorithm, encryption mode, key size, secret key, and initialization vector), assuming asymmetric encryption is used. In the demo below, you can see how I discovered additional parameters (balance, is_admin) in the API response, captured the registration API request, despite it being sent over HTTPS from the interface, added the discovered parameters, and successfully inflated my balance to 50 billion and also escalated my privileges to admin, and ultimately deleted the accounts of two live users/customers. In the second slide, I captured an API traffic of a bank app, and you can see how difficult the payloads are to read.
2:38

This is a standard practice for almost all Tier-1 banking applications in Nigeria, and for some fintech applications I’ve previously performed pentests on. Client-side encryption isn’t a total waste, or a waste of compute, as some people have claimed, but rather a measure to protect against API tampering or API request/response manipulation between the client and the server when implemented properly. Even with HTTPS, attackers can capture a decrypted version of web or mobile API data in transit because the browser and the server establish a level of trust during the TLS handshake. Attackers can leverage this trust to capture & proxy already-decrypted traffic, tamper with it, and then forward it to the server. This allows them to override what the user interface or client is originally supposed to send and replace it with data of their choosing. That is why validation needs to be performed on both the client and the server side. To wrap up, encrypting API requests and responses makes it significantly harder for attackers to tamper with data, even if they capture the traffic, unless they have access to the encryption details (algorithm, encryption mode, key size, secret key, and initialization vector), assuming asymmetric encryption is used. In the demo below, you can see how I discovered additional parameters (balance, is_admin) in the API response, captured the registration API request, despite it being sent over HTTPS from the interface, added the discovered parameters, and successfully inflated my balance to 50 billion and also escalated my privileges to admin, and ultimately deleted the accounts of two live users/customers. In the second slide, I captured an API traffic of a bank app, and you can see how difficult the payloads are to read.

Ghost St Badmus

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Consistent Hashing Explained Simply Let's break down consistent hashing in a way that's easy to get. It's a handy technique for distributing data evenly across servers. And if you're more of a visual learner, don't miss the attached video which brings this concept to life. Here's how it works: We don't just hash our data keys; we also hash the server names using the same method. This puts everything on a scale, known as the hash space, which we think of as a big circular ring. So, imagine we've got a few servers. We hash them and place them on this ring. We do the same with our data, hashing it by key. To figure out where to store a piece of data, we travel clockwise on the ring from where the data lands until we bump into a server. Like, data key 0 might end up on server 0, and key 1 on server 1. Adding a new server? Only the data near the new server needs to shift. And if a server has to go? Only the data it was holding needs a new spot. But there's a little challenge: sometimes, data doesn't spread out evenly. The fix? Virtual nodes. They're like mini-replicas of a server placed around the ring. The more virtual nodes we have, the better the balance. But there's a catch: more virtual nodes mean more memory usage for tracking them, and it can make the whole system a bit more complex to manage. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages):

Sahn Lam

36,787 görüntüleme • 2 yıl önce

I just found out OpenAI gives you $50,000 in free API credits if you do one thing in settings. 🤯 It's called the Data Sharing Program. No free trial exists for the API. But this does. Go to your OpenAI Dashboard → Data Controls → Sharing. Opt in. You get $50,000 in credits for their latest models or 2.5 million tokens for the rest. The catch: your data gets used by OpenAI for training and improvements. So don't use this for client work or anything sensitive. Use it for learning, side projects, and experiments.
0:44

I just found out OpenAI gives you $50,000 in free API credits if you do one thing in settings. 🤯 It's called the Data Sharing Program. No free trial exists for the API. But this does. Go to your OpenAI Dashboard → Data Controls → Sharing. Opt in. You get $50,000 in credits for their latest models or 2.5 million tokens for the rest. The catch: your data gets used by OpenAI for training and improvements. So don't use this for client work or anything sensitive. Use it for learning, side projects, and experiments.

Vaibhav Sisinty

275,641 görüntüleme • 2 ay önce

Iranian researchers develop AI that 'sees' cancer more clearly than ever before The new system combines deep learning and smart data reduction to spot breast, brain, and blood cancers with greater accuracy 🔸 The AI first isolates suspicious areas in medical scans using advanced image processing 🔸 It then extracts key features — including shape, location, and texture — of potential tumors 🔸 Smart filters remove unnecessary data, keeping only the most meaningful patterns 🔸 Special fuzzy logic algorithms handle tricky cases where healthy and cancerous tissue look almost identical This breakthrough could lead to earlier detection, better treatment decisions, and ultimatel
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Iranian researchers develop AI that 'sees' cancer more clearly than ever before The new system combines deep learning and smart data reduction to spot breast, brain, and blood cancers with greater accuracy 🔸 The AI first isolates suspicious areas in medical scans using advanced image processing 🔸 It then extracts key features — including shape, location, and texture — of potential tumors 🔸 Smart filters remove unnecessary data, keeping only the most meaningful patterns 🔸 Special fuzzy logic algorithms handle tricky cases where healthy and cancerous tissue look almost identical This breakthrough could lead to earlier detection, better treatment decisions, and ultimatel

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15,465 görüntüleme • 20 gün önce

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MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

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142,696 görüntüleme • 1 yıl önce

The 2nd edition of my "React Key Concepts" book is available now"! Fully updated for React 19, including brand-new chapters on RSC, form & server actions, Suspense and much more! If you, like me, like picking up a book for learning from time to time and you're (like me), convinced that we'll still need (React) devs in a couple of years, this book will be perfect for you! PLUS: There are special bonus offers & packages available now for the launch!
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The 2nd edition of my "React Key Concepts" book is available now"! Fully updated for React 19, including brand-new chapters on RSC, form & server actions, Suspense and much more! If you, like me, like picking up a book for learning from time to time and you're (like me), convinced that we'll still need (React) devs in a couple of years, this book will be perfect for you! PLUS: There are special bonus offers & packages available now for the launch!

Maximilian

48,344 görüntüleme • 1 yıl önce

Right now, when you send a query to an LLM, it gets decrypted on the server. The LLM sees your data in plain text. Prof. Ajay Joshi (BU, CipherSonic AI ) on fully homomorphic encryption, which may be key for the future of AI privacy: how we can compute on data without ever decrypting it. The catch: it's a brutally memory-bound workload. Exactly the bottleneck wafer-scale was built to solve.
0:56

Right now, when you send a query to an LLM, it gets decrypted on the server. The LLM sees your data in plain text. Prof. Ajay Joshi (BU, CipherSonic AI ) on fully homomorphic encryption, which may be key for the future of AI privacy: how we can compute on data without ever decrypting it. The catch: it's a brutally memory-bound workload. Exactly the bottleneck wafer-scale was built to solve.

Cerebras

293,561 görüntüleme • 1 ay önce

PSA: Groq Inc now supports remote MCP server integration (BETA). ⚡️ LLMs are only as good as the context you give them and MCP enables you to connect them directly to live APIs, databases, and tools. Now your models can pull fresh data, perform actions, AND respond in seconds.
7:30

PSA: Groq Inc now supports remote MCP server integration (BETA). ⚡️ LLMs are only as good as the context you give them and MCP enables you to connect them directly to live APIs, databases, and tools. Now your models can pull fresh data, perform actions, AND respond in seconds.

Hatice Ozen

13,829 görüntüleme • 10 ay önce

You can now connect your React application to any MCP server! And it only takes a single command: $ npx copilotkit@latest init -m MCP This is massive for anyone building React applications! Using the CopilotKit🪁 MCP Client Component, you can get an MCP Client chat component in your application and instant access to any MCP server out there! It doesn't get easier than this. Check out the link below for instructions on how to proceed ↓
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You can now connect your React application to any MCP server! And it only takes a single command: $ npx copilotkit@latest init -m MCP This is massive for anyone building React applications! Using the CopilotKit🪁 MCP Client Component, you can get an MCP Client chat component in your application and instant access to any MCP server out there! It doesn't get easier than this. Check out the link below for instructions on how to proceed ↓

Santiago

39,944 görüntüleme • 1 yıl önce

The base64 approach to binary file transfer is corrupted at 175KB and fails entirely at 20MB. The Box MCP server handles both in under 30 seconds. See it in action working end to end in Anthropic's Claude. Summarize an annual report PDF, add a key takeaways page, and re-upload as a new version. Signed URLs. No binary data in the LLM context. Full audit trail. Watch here. 👇
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The base64 approach to binary file transfer is corrupted at 175KB and fails entirely at 20MB. The Box MCP server handles both in under 30 seconds. See it in action working end to end in Anthropic's Claude. Summarize an annual report PDF, add a key takeaways page, and re-upload as a new version. Signed URLs. No binary data in the LLM context. Full audit trail. Watch here. 👇

Box

203,602 görüntüleme • 2 ay önce

If you’re playing on MonkeyTilt, the outcome might already be decided BEFORE you bet “The cryptography behind every bet has always been sound” We tested it It isn’t... we sent two different client seeds for the same nonce same result both times the server is ignoring the client seed entirely the player has ZERO influence on outcomes — what we did — opened the MonkeyTilt provably fair verifier with a revealed server seed entered nonce 1 → got 3.04x changed the client seed same server seed, same nonce → 3.04x changed it again → 3.04x the client seed has ZERO effect on the outcome — why this matters — in a provably fair system the outcome is derived from: -server seed -client seed -nonce the client seed exists so the player contributes randomness the server CAN’T predict if it’s ignored the server knows every outcome BEFORE you bet the hash commitment proves nothing because the server already controls all the inputs — not a frontend bug — we checked the network traffic the verifier sends a WebSocket request with: -client seed -server seed -nonce the server receives different client seeds and returns the same result anyway request 1 → client_seed “Clientseed123456789” → 3.04x request 2 → client_seed “NEWSEED12345” → 3.04x this is the server ignoring input not a display issue — live bet traffic — placing a Limbo bet returns: -random_multiplier -total_payout -round_closed that’s it no hash no seed reference no nonce no proof seeds are fetched from a separate endpoint game engine and seed system are DISCONNECTED — nonce verification — you can only verify nonces you’ve already played in a real system all outcomes are predetermined once the server seed is committed - they’re deterministic blocking future nonces suggests results don’t exist until you bet — summary — -client seed ignored by server -no cryptographic proof in bet responses -game engine and seed system DISCONNECTED -nonce verification restricted this isn’t provably fair it looks like provably fair UI on top of server side RNG MonkeyTilt should probably address this anyone can verify this themselves in 60 seconds - open the verifier, change the client seed, watch the result stay the same
0:46

If you’re playing on MonkeyTilt, the outcome might already be decided BEFORE you bet “The cryptography behind every bet has always been sound” We tested it It isn’t... we sent two different client seeds for the same nonce same result both times the server is ignoring the client seed entirely the player has ZERO influence on outcomes — what we did — opened the MonkeyTilt provably fair verifier with a revealed server seed entered nonce 1 → got 3.04x changed the client seed same server seed, same nonce → 3.04x changed it again → 3.04x the client seed has ZERO effect on the outcome — why this matters — in a provably fair system the outcome is derived from: -server seed -client seed -nonce the client seed exists so the player contributes randomness the server CAN’T predict if it’s ignored the server knows every outcome BEFORE you bet the hash commitment proves nothing because the server already controls all the inputs — not a frontend bug — we checked the network traffic the verifier sends a WebSocket request with: -client seed -server seed -nonce the server receives different client seeds and returns the same result anyway request 1 → client_seed “Clientseed123456789” → 3.04x request 2 → client_seed “NEWSEED12345” → 3.04x this is the server ignoring input not a display issue — live bet traffic — placing a Limbo bet returns: -random_multiplier -total_payout -round_closed that’s it no hash no seed reference no nonce no proof seeds are fetched from a separate endpoint game engine and seed system are DISCONNECTED — nonce verification — you can only verify nonces you’ve already played in a real system all outcomes are predetermined once the server seed is committed - they’re deterministic blocking future nonces suggests results don’t exist until you bet — summary — -client seed ignored by server -no cryptographic proof in bet responses -game engine and seed system DISCONNECTED -nonce verification restricted this isn’t provably fair it looks like provably fair UI on top of server side RNG MonkeyTilt should probably address this anyone can verify this themselves in 60 seconds - open the verifier, change the client seed, watch the result stay the same

CoinBets🔍

20,973 görüntüleme • 4 ay önce

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?
0:51

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?

Zach Wilson

20,368 görüntüleme • 1 yıl önce

Problem: After working for a few months in open source, I noticed that understanding issues is often very tough, and random OS files make it a nightmare for newbies. Solution: So here we go I created It explains any open-source repo file line by line with flowcharts, explains open-source issues, and provides a dedicated step-by-step solution plan. It also helps you write PR content in multiple languages. Built for the Apify × Lingo.dev Multilingual Actors Hackathon You can start using it by adding a Gemini or Groq API key. For more detailed explanations and better data fetching, you can add an Apify API key. For in-app translations, it uses Lingo. Even if you don’t believe in the app, you can check the repo code or fork it and deploy it yourself (SQL query provided) and use it unlimited times. Enjoy. Your Problem solved.
1:50

Problem: After working for a few months in open source, I noticed that understanding issues is often very tough, and random OS files make it a nightmare for newbies. Solution: So here we go I created It explains any open-source repo file line by line with flowcharts, explains open-source issues, and provides a dedicated step-by-step solution plan. It also helps you write PR content in multiple languages. Built for the Apify × Lingo.dev Multilingual Actors Hackathon You can start using it by adding a Gemini or Groq API key. For more detailed explanations and better data fetching, you can add an Apify API key. For in-app translations, it uses Lingo. Even if you don’t believe in the app, you can check the repo code or fork it and deploy it yourself (SQL query provided) and use it unlimited times. Enjoy. Your Problem solved.

Sk Akram

30,264 görüntüleme • 7 ay önce

Duel launched Rock Paper Scissors - no house edge, just live 1v1 PvP. The whole point: 100% RTP. Nobody makes money on the round itself - it's just a fair 1:1 online matchup. You create a game, set your bet size and accepted currencies, or join an existing one. How the PF works: both players' picks get hashed (SHA256 + a client key) and locked in before either side reveals. After the round, both keys and picks are published - anyone can recompute the hashes and verify neither side changed their pick mid-game. True 1v1, no house in between. Maybe we can do RPS tournaments for now - no more giveaways, just double or lose your prize.
0:20

Duel launched Rock Paper Scissors - no house edge, just live 1v1 PvP. The whole point: 100% RTP. Nobody makes money on the round itself - it's just a fair 1:1 online matchup. You create a game, set your bet size and accepted currencies, or join an existing one. How the PF works: both players' picks get hashed (SHA256 + a client key) and locked in before either side reveals. After the round, both keys and picks are published - anyone can recompute the hashes and verify neither side changed their pick mid-game. True 1v1, no house in between. Maybe we can do RPS tournaments for now - no more giveaways, just double or lose your prize.

seb

164,111 görüntüleme • 2 ay önce

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
2:43

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,130 görüntüleme • 1 yıl önce

As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.
0:36

As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.

Sui_Builds

12,853 görüntüleme • 7 ay önce

Everything you’ve ever wanted to know about SAFe and the product owner role, with Melissa Perri (Melissa Perri) 🌶️ Melissa is the author of Escaping the Build Trap, host of the Product Thinking Podcast, and the founder of Product Institute where she works with startups, Fortune 50 companies, and everything in between to help them build better products. In our conversation, we discuss: 🔸 The evolution of and problems with the SAFe framework 🔸 The history of the product owner role 🔸 The differences between product owners and product managers 🔸 How to transition from product owner to product manager 🔸 How large non-tech companies can improve their product practices 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 pendo.io — The only all-in-one product experience platform for any type of application: 🏆 OneSchema — Import CSV data 10x faster: 🏆 Coda — The all-in-one collaborative workspace:
1:05

Everything you’ve ever wanted to know about SAFe and the product owner role, with Melissa Perri (Melissa Perri) 🌶️ Melissa is the author of Escaping the Build Trap, host of the Product Thinking Podcast, and the founder of Product Institute where she works with startups, Fortune 50 companies, and everything in between to help them build better products. In our conversation, we discuss: 🔸 The evolution of and problems with the SAFe framework 🔸 The history of the product owner role 🔸 The differences between product owners and product managers 🔸 How to transition from product owner to product manager 🔸 How large non-tech companies can improve their product practices 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 pendo.io — The only all-in-one product experience platform for any type of application: 🏆 OneSchema — Import CSV data 10x faster: 🏆 Coda — The all-in-one collaborative workspace:

Lenny Rachitsky

16,905 görüntüleme • 1 yıl önce

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here:
2:01

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here:

Andrew Ng

77,636 görüntüleme • 9 ay önce