Our course "Windows Legacy & PnP Driver Programming" Get started into writing windows drivers and dive into kernel development. Develop production grade software driver that handles pnp and power management. Course Contents: 🛠️ Windows VM setup and Hello World Driver 🛠️ Exploring DRIVER_OBJECT 🛠️ Creating Devices & Symbolic Links 🛠️ Dispatch Routines 🛠️ IO Request Packet 🛠️ METHOD IO 🛠️ Upper Filter Drivers 🛠️ Creating IRPs 🛠️ Interrupts and IRQL 🛠️ Synchronization Primitives like Spinlock, mutex, semaphore, events etc 🛠️ StartIO 🛠️ Deferred Procedure Call (DPC) 🛠️ Workitem and IRP Queue Draining 🛠️ Cancel Safe Queues 🛠️ Registry Operations 🛠️ Plug n Play Theory 🛠️ AddDevice() 🛠️ Handling PnP IRPs like IRP_MN_START_DEVICE, IRP_MN_STOP_DEVICE, IRP_MN_REMOVE_DEVICE etc. 🛠️ Usermode Asynchronous IOCTL 🛠️ Installing PnP driver with devcon 🛠️ Auto updating PnP driver 🛠️ PnPutil 🛠️ CSQ in PnP driver 🛠️ RemoveLocks 🛠️ Raw & Translated Resources 🛠️ Filter Resource requirements 🛠️ Device Usage Notifications 🛠️ DO_POWER_PAGABLE 🛠️ Power Basics 🛠️ Handling IRP_MN_QUERY_CAPABILITIES 🛠️ Handling IRP_MN_QUERY_POWER, IRP_MN_SET_POWER Price: $99 only Bonus contents: Full code snippets Exclusive access to high quality onenote diagrams in pdf format. Get it at $99 only: udemy.com/course/windows-leg… #infosec #cybersecurity #windowsinternals #driverprogramming #hackthebox #tryhackme

A Bet We Made in 2018 This is me and Vitalik in 2018. ETH Devcon. We were at a table talking about sharding and how blockchains could actually scale by running execution in parallel. At the time, Ethereum and Elrond had the same thesis. Both believed execution sharding was the path forward. Since then, Ethereum changed direction multiple times. Sharding gave way to rollups. Rollups gave way to "we need to scale L1 again." The roadmap shifted, the promises changed, the technical approach pivoted more than once. And through all of it - the Ethereum community stayed. They gave the team the space to find the right path. They didn't kill the project when the roadmap changed. They believed the builders would figure it out. That patience is what kept Ethereum alive. Not the technology. The community. ─── We made a different bet. We bet on execution sharding from day one and we never left that path. Adaptive state sharding shipped on mainnet in 2020. Three execution shards plus a metachain. State, network, transactions - all sharded from genesis. Supernova shipped this year. Consensus decoupled from execution. 600ms blocks. achieved 88ms finality. 120,000 TPS burst mode on the same validator hardware. 3,200 validators on consumer-grade machines. 171,000 commits. #6 in the entire industry. Ahead of Solana, Avalanche, Sui, Polygon, Aptos, and TON. Tier-one infrastructure providers on board, distribution partners like Cointelegraph are running validator nodes. Stripe's Machine Payments Protocol is settling on MultiversX. EGLD is named in an Arizona state strategic reserve bill. The agentic commerce stack - x402, MPP, MX-8004, UCP, ACP, MCP - is live on one chain. xMoney is building regulated stablecoin issuance under MiCA. The infrastructure underneath has never been stronger. ─── And yet the market prices EGLD like none of this exists. Every blockchain community is going through the same thing right now. EGLD is down. ETH is down. SOL is down. The market doesn't differentiate between chains that shipped paradigm-shifting infrastructure and chains that shipped a landing page. But here's what's different about right now. Bitcoin survived 2014. Ethereum survived 2018. Solana survived 2022. In each case, the community stayed long enough for the technology to matter. In each case, the community that held wrote the next chapter. And in each case, the ones who fractured, who turned on their own builders, who let the loudest voices - the ones who already sold - define the narrative? Those communities don't exist anymore. You don't remember their names. The perception of value has been distorted. By cycles. By speculation. By narratives that reward noise over engineering. And by us - divided when we should be united. Turning on each other instead of building forward together. Look at what's underneath. Not the chart. The infrastructure. 88ms finality. 120K TPS. #6 in developer activity globally. Tier-one infrastructure partners. Stripe settling on-chain. EGLD in US state legislation. Regulated stablecoins under MiCA. An agentic commerce stack that Visa, Coinbase, and Mastercard are racing to replicate. This is not a project searching for relevance. This is a project that shipped the relevance and is waiting for its community to match it. If this fails, it won't be because the technology wasn't good enough. It will be because we weren't united enough to carry it forward. I've been here since 2018. That table with Vitalik. Every pivot, every cycle, every doubt. Still here. Still building. Now it's your turn. Not to believe. To show up.

I believe these experiences can help me contribute at Devcon through registration support, information desks, attendee assistance, logistics, crowd management, and team coordination while helping create a welcoming experience for everyone attending.

Applied for volunteer at @EFDevcon India 🇮🇳 Following Devcon since Devcon 7, which sparked my interest in ETH and Web3 Already filled the volunteer application on the very same day it was released 🙌 Excited to contribute, learn, and connect with builders worldwide.🤝🏻🇮🇳

Yoo yoo!! I guess Devcon is on

🚨 PalantirがAIで「現場最前線のエンジニア」を根本から進化させた — AI FDEの本当の衝撃 2026年3月に開催されたPalantir DevCon 5で、ソフトウェアエンジニアのAnkit Shankar氏とColton Rusch氏がAI FDE（AI Forward Deployed Engineer）の新機能をライブデモしました。 これは「Forward Deployed Engineering（最前線展開エンジニアリング）」というPalantirの強力な文化を、AIエージェントが人間と同じレベルで担えるようになった画期的な発表です。 Forward Deployed Engineer（FDE）とは？ Palantirのエンジニアは、ただコードを書くだけでなく、顧客の現場に直接入り込み、複雑なデータや業務課題をPalantirのプラットフォーム（Foundry / AIP）で実際に使える形にカスタマイズします。 大企業・政府・規制産業で特に価値を発揮する「実践派エンジニア」です。 AI FDEはその仕事をAIが自律的にこなすようになりました。 人間の指示を自然言語で受け取り、探索・作成・テスト・改善の全サイクルを安全に回すのです。 デモの核心：工場での安全事故トリアージ（分類・対応判断）システム構築 デモでは、50件の事故報告データとOSHA（米安全規制）資料を基に、事故の種類・深刻度・必要な対応を自動判断するシステムをその場でAIが作る様子が公開されました。 ステップバイステップの流れ（AIが自動で進めたこと） 1. 自然言語指示からスタート 
人間：「これらのデータと規制資料を使って、事故を自動分類・深刻度判断するロジックを作って」 2. AIP Logic関数の自動作成 ◦PalantirのAIP Logic（LLMを活用したノーコード/ローコードの関数作成環境）を使って、Ontology（データ構造）を活用したビジネスロジックを自動生成。 ◦内部でSemantic Search（意味検索）を使い、規制資料を適切に参照。 3. Evals（評価テスト）の自動作成と実行 ◦AI自身が多様なテストケースを作成・実行。 ◦初回は精度が低かったが、失敗原因を分析して改善。 4. Branch-aware continuous loop（安全な連続改善ループ） 
これが最大の革新点！ ◦Gitのブランチのように実験用の安全な環境で変更を試す。 ◦失敗しても本番環境に影響なし、簡単にロールバック可能。 ◦「書いて → テスト → デバッグ → 修正」をAIが連続で繰り返す。 ◦コンテキストを保持しながら、どんどん賢くなる。 5. Agents building Agents（AIがAIを作る） 
ユーザーからのフィードバックが曖昧な場合、別の支援AIエージェントを自動作成。 
より具体的な質問を投げて、質の高いデータを集め、さらなる改善を促す。 なぜこれが「真実として」世界を変えるのか •今までのAIコード生成ツールは「コードを書く」までは得意でしたが、顧客現場のフィードバックを活かして継続的に改善する部分が弱かった。 •AI FDEはエンジニアリングの全ライフサイクル（初稿作成 → テスト → 本番準備 → 運用改善）をAI主導で回せるようにした。 •人間のFDEは「最終的なビジネス判断」や「複雑な例外対応」に集中できるようになり、生産性が劇的に向上。 •安全性・信頼性・スケーラビリティを重視したPalantirらしい設計（オーケストレーションと安全レールが鍵）。 特に製造業、医療、金融、政府などデータと規制が複雑に絡む業界で革命的です。 あなたの3Dスキャン・モデリング事業のように、顧客現場に素早く高品質なカスタムソリューションを提供する場面で、AI FDEは強力な武器になるでしょう。 私の考え：これは「AIが人間の仕事を奪う」のではなく、Palantirの人間中心の現場力をAIでスケールさせる本物のブレイクスルーです。 AI時代の本質は「ツールをどう安全に現場に展開するか」。 Palantirはそこを先取りしています。 世界中のエンジニアやビジネスパーソンが注目すべき進化だと思います。

Join @OrnellaWeb3 for a sneak peek of @EFDevcon India at the Berlin Ethereum Day! Explore how Devcon is evolving as a key gathering for Ethereum’s global ecosystem, and what it signals for long-term coordination around Ethereum’s core values. Registration 👇

Does Devcon exsist.

Recognized at PCI-SIG DevCon 2026, Paul Cassidy joins a growing record of Synopsys leadership honored for advancing #PCIe. Read how Synopsys is committed to standards development from specification through interoperability and deployment: bit.ly/4gce8nt

Right but an important criteria for picking Devcon locations is enthusiasm of the local community & their ability to sustain the movement after event is over General interest wise your preferences are certainly a data point, but not for the criteria of local community

The fact that most new yorkers either didn't get a ticket for ethconf or just attended one day out of 3 means it's probably a bad bet to try Devcon NY

New York has shipped Remilia (the PFP you wear), every lindy meme from $PEPE to base:0x2a06a17cbc6d0032cac2c6696da90f29d39a1a29 , as well as countless DeFi and infra projects on ETH It 100% holds up as a good place for Devcon based on your criteria

Yup strength and enthusiasm of local community is key for choosing Devcon locations And eth mumbai showed it was there

Do Mumbai locals need to show interest and engagement for Devcon Mumbai? Cause I’d go to Devcon New York, but not Devcon Mumbai

I don't know if we can cite as a success that Devcon NY will have people going to side events Until the NY locals shows more interest & engagement, Devcon NY is a bad idea

Look at the data from new yorkers themselves! They would block out time for a different city but not if it's in their own Without local community support Devcon doesn't work, and new york doesn't have it right now Doing it top down is failure

definitely good evidence for why hosting a devcon in a major city like NY or SF isn't as good as getting those folks to fly out to a place where they wouldnt otherwise travel!

i know @fede_intern has been a big proponent of Devcon NY, curious if your opinion is changed now or still think its a good idea

Yup that's what I found surprising, that the people a the conference were mostly from outside NY Tickets started around Devcon rates and then went up to double that, which is still cheaper than hotel spend by non locals. Hence my surprise at less new york people being there

In the age of AI, taste is the differentiator. But how do you channel taste into the things you build? @bkase_ and I discuss 4 emerging practices: Shifting left inside agent harnesses, expert language, tool interfaces, and oracles. 01:54 @_lopopolo's (OpenAI) talk on harness engineering and shifting left 05:45 Birgitta Böckeler on guides and sensors in harness engineering 13:11 @rauchg (Vercel) on mastery of language in the age of AI 17:04 @bkase_ on borrowing expert language from an additional domain (algebra) with better abstractions 26:06 MCP, CLI tools, Bash, and code mode 32:07 @trq212 (Anthropic) on how Claude can express a workflow through code 35:47 @mattzcarey (Cloudflare) on a code-mode-style approach to search 36:37 @bkase_ on when using code mode is better 40:48 @justincormack (former CTO at Docker) on building an S3 clone using an oracle at @tessl_io's AI Native DevCon London Would love to hear what practices others are using to channel taste into what they build.