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"The efficiency on top of the efficacy because it is critical that we will achieve this balance of cost and performance. And in addition to that, I think one more aspect we are putting emphasis is actually how, in the new era of AI, all pervasive intelligence and pervasive connectivity, how do we bring human, AI, and machine into a cohesive coexistence and also cohesive collaboration so that we are able to have actually the AI machine support human, empower human, and at the same time. We look at actually the design of the workflows so that we are able to extract out the value, at the same time, also elevate the human to do more creative works. So this is one aspect. I think we have been hearing a lot, but how this will move forward is some efforts have started, and certainly we look forward to gathering more ecosystem partners to embark on this journey together." - Dr Sun Sumei, Executive Director, Institute of Infocomm Research, ASTAR Fresh out of the studio, Dr. Sun Sumei, Executive Director of the Institute for Infocomm Research (I2R) at Singapore's ASTAR shares her perspectives on AI's fast-paced evolution and its broader impact on the future. With over a decade dedicated to AI research, Dr. Sumei reflects on I2R's journey from big data analytics to the era of Generative AI, emphasizing large language models designed for Southeast Asia. Advocating for responsible AI, Dr. Sumei prioritizes societal benefit and sustainability over sheer technical ambition, urging a balanced, systematic approach to distinguish valuable applications from mere hype. Last but not least she shares her vision of AI as a force to enhance human creativity and address real-world challenges through sustainable innovation." Audio Episode Highlights: [0:46] - Introduction to Sun Sumei and her role at ASTAR [2:09] - Sun Sumei's journey from China to Singapore [2:50] - The mission of the Institute for Infocomm Research (I2R) [4:00] - Research at I2R blending innovation with commercial applications [4:11] - Career lessons from Sun Sumei's extensive experience in science and technology [6:32] - The evolution of AI and its rapid advancements [7:15] - Striking a balance between performance and cost in AI [8:11] - Collaborating with Singapore Airlines on AI for fleet optimization [9:55] - Focus on multimodal AI and large language model development [11:26] - AI for manufacturing: The AIM initiative [12:20] - MerLion: I2R multi-modal LLM in the works [13:27] - The future of AI in scientific discovery and its potential for sustainability [16:03] - The balance between necessary and unnecessary AI applications [16:55] - Responsible AI: Navigating the curiosity phase [19:10] - Achieving success in AI through efficiency and model optimization [20:52] - What constitutes success in AI: Technical breakthroughs vs. societal impact [22:48] - AI for good: Contributing to humanity and sustainability [24:02] - The importance of a systematic approach to AI [24:53] - Closing remarks and future expectations for AI You can find Dr Sun Sumei here in ASTAR: https://www.a-star.edu.sg/i2r/about-i2r/i2r-management/sun-sumei and her LinkedIn: https://www.linkedin.com/in/sumei-sun-8590814/?originalSubdomain=sg Podcast Information: Bernard Leong hosts and produces the show. Proper credits for the intro and end music: "Energetic Sports Drive" and the episode is mixed & edited in both video and audio format by G. Thomas Craig Analyse Asia Main Site: https://analyse.asia Analyse Asia Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Asia Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Asia YouTube: https://www.youtube.com/@AnalyseAsia Analyse Asia LinkedIn: https://www.linkedin.com/company/analyse-asia/ Analyse Asia X (formerly known as Twitter): https://twitter.com/analyseasia Analyse Asia Threads: https://www.threads.net/@analyseasia Sign Up for Our This Week in Asia Newsletter: https://www.analyse.asia/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288
FAMILY DISPUTE OVER MY MOMS ESTATE In this special episode we jby Jenica & A Star, who are both Sickle Cell advocates, to teach and educate us on what we can do to help raise awareness of Sickle Cell. In Part 1 of this Episode the boys play, Don't Google it and give advice on a dilemma around a lady who sadly recently lost her mother and is being forced to pay her debts by her Grandmother from her moms estate. As always, please comment below with your thoughts and don't forget to Like, Share And Subscribe
While the SEC is going after crypto's top American companies, Asia continues to push web3 adoption further. Sony Group partnered with Soneium, an Ethereum Layer 2 blockchain, with the mission focused on mainstream adoption.~This episode is sponsored by Tangem~Tangem ➜ https://bit.ly/TangemPBNUse Code: "PBN" for Additional Discounts!00:00 Intro00:22 Sponsor Tangem01:53 SEC going after gaming assets?02:39 Gaming NFT stats03:06 WebX event speakers03:55 Soneuim & Sony bringing blockchain mainstream05:06 Monthly active users05:34 Sony releases testnet incubator07:11 Sony partners w/Transak10:00 Oasys partners with SBI Holdings#crypto #sony #web3gaming Soneium
Over the past week, MATIC's price surged 28% as investors anticipate the upcoming token migration from MATIC to POL. Flow is also set to witness the arrival of the Crescendo Network Upgrade, which will be launched onthe same day... September 4th.~This Episode is Sponsored By Coinbase~ Get up to $200 for getting started on Coinbase➜ https://bit.ly/CBARRON 00:00 intro00:20 Sponsor: Coinbase00:42 September 4th $POL 01:34 What is AggLayer?02:52 What will be enabled?04:03 Polygon VPU Chips Coming05:58 Astar Network06:20 Wilder World & Gaming06:41 Flow September 4th07:25 Flow Will Improve Onboarding For Ethereum09:44 Tokenized Assets & Interoperability10:52 Chipotle Coming To Flow?12:37 outro#Crypto #ethereum #nft~The Biggest Day in Crypto Coming?
BRAVE Southeast Asia Tech Podcast: DJ Tan, CTO & Cofounder of Prefer, and Jeremy Au talked about three main topics: 1. Government Food Scientist: DJ recounted his early fascination with science and his studies in chemistry at UCL, leading to a focus on food science at Singapore's ASTAR government lab. He shared how his academic and laboratory experiences shaped his decision to experiment at the frontier of what humans eat and drink and merge rigorous scientific methods with innovative culinary practices. He discussed the challenges that novel food products face, particularly in achieving cost parity and consumer acceptance regarding taste. 2. Prince of Fermentation: Transitioning from a chemistry enthusiast to a fermentation expert, DJ detailed his journey from organic synthesis to leveraging his expertise to transform flavor profiles in partnership with alcohol mixologists and chefs. Fermentation is a strategic solution that enhances and creates new flavors while potentially reducing production costs. He also shared how his title was accidentally coined, and how he feels about this personal brand today. 3. Coffee Without Beans: DJ discussed the inception of Prefer, a startup aimed at creating sustainable food solutions, beginning with bean-free coffee. He outlined the strategic positioning in the market, focusing on consumer benefits and business challenges. Prefer's goal is to revolutionize the coffee industry by offering an environmentally friendly alternative that maintains taste and convenience. The conversation also touched on scaling production, sustainable packaging, and the impact of their business model on the food industry's adaptation to climate change. Jeremy and DJ also talked about the technical challenges in food science, consumer trends towards innovative food products, and the impact of global environmental changes on agricultural practices. Watch, listen or read the full insight at https://www.bravesea.com/blog/dj-tan Nonton, dengar atau baca wawasan lengkapnya di https://www.bravesea.com/blog/dj-tan-id 观看、收听或阅读全文，请访问 https://www.bravesea.com/blog/dj-tan-cn Xem, nghe hoặc đọc toàn bộ thông tin chi tiết tại https://www.bravesea.com/blog/dj-tan-vn Get transcripts, startup resources & community discussions at www.bravesea.com WhatsApp: https://chat.whatsapp.com/CeL3ywi7yOWFd8HTo6yzde TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter: https://twitter.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter: https://twitter.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea English: Spotify | YouTube | Apple Podcasts Bahasa Indonesia: Spotify | YouTube | Apple Podcasts Chinese: Spotify | YouTube | Apple Podcasts Join us at Geeks on a Beach! Use the code "BRAVESEA" for a 45% discount for the first 10 registrations, and 35% off for the next ones.
Paul and Skip have an audience with a man who's films have racked up an astonishing 47 Oscar noms over the course of his incredible career. During his long tenure at Warner Brothers, he helped bring classics like Goodfellas, JFK, Unforgiven, Heat, L.A. Confidential, and Twister to the big screen before embarking on an ultra-successful run as an independent producer. His reputation as a Hollywood visionary stems from the fact that he's seen it all. During this candid interview, Gerber recounts his youth as a showbiz kid, rubbing shoulders with Paul's Rat Pack friends alongside his agent father. He shares tales from his early days in the music industry, working with rock legends like Joni Mitchell and Neil Young as well as developing acts like Devo and The Cars in the burgeoning post-punk and New Wave scene. Gerber also shares rare insights into his decades-long creative alliance with Clint Eastwood, and explains the long journey to get the blockbuster ‘A Star Is Born' off the ground. See omnystudio.com/listener for privacy information.
Sony crypto exchange is set to launch soon with the acquisition of Amber Japan, diversifying its $100 billion portfolio. Sony's commercial subsidiary, Sony Network Communications, collaborated with the Japanese blockchain startup Startale Labs in 2021 to develop Sony's public blockchain network.~This episode is sponsored by Wilder World~Wishlist Wilder World on EPIC Games Store ➜ https://www.wilderworld.com/00:00 Intro00:17 Sponsor Wilder World00:56 Sony launching a crypto exchange + Whalefin02:20 2 Years ago we called it03:20 Astar CEO on Japan & Sony04:28 Entering execution phase05:05 Sony Network Communications05:21 Prime Minister06:00 Sony Connect App x Securitize07:00 Astar CEO: Polygon vs Astar in Japan08:03 Sony Super Fungible Tokens patent08:17 Ghostbusters NFT x Stoner Cats08:50 Quest vs iPhone09:45 Sony VR Disappoints10:05 Sony Ghostbusters VR10:32 Mark Zuckerberg: (New Interview) mega AR reveal incoming12:10 Yat Siu: Next ETF will be a gaming token basket13:10 Yat Siu: 100mil users incoming14:00 Meanwhile in the U.S. (select blockchain committee wyoming)16:30 MiCA Launches17:07 Outro#crypto #ethereum #nft~Sony Crypto Exchange Launching on $ASTR!
Doug Ingle, fondateur, chanteur et organiste du groupe de psych rock classique Iron Butterfly, est décédé à l'âge de 78 ans, dernier membre survivant de la formation classique du groupe. Charlie Colin, le bassiste fondateur du groupe Train, qui a participé à la chanson "Drops of Jupiter" est décédé à l'âge de 58 ans. Les Black Keys ont rompu le silence après avoir annulé la totalité de la prochaine tournée nord-américaine, promettant aux fans une "expérience plus excitante et intime" que les grandes salles prévues. Très beau moment pour les personnes présentes au concert de Pearl Jam au BottleRock de Napa Valley ce samedi 25 mai, l'acteur Bradley Cooper a rejoint le groupe pour interpréter le titre "Maybe It's Time" qu'il chante dans le film ‘'A Star is Born''. Sting, le guitariste/ collaborateur de longue date Dominic Miller et le batteur Chris Maas, sont montés sur la scène de Dresde, en Allemagne, samedi soir, pour donner le coup d'envoi de la tournée "Sting 3.0", livrant un set de tubes et de morceaux moins connus, dont du Police. Le célèbre batteur de Dream Theater, Mike Portnoy a relevé le défi d'apprendre à jouer une chanson de Tool, qui, selon lui, fait passer "Dream Theater pour Weezer". Mots-Clés : tube, 1968 , In-A-Gadda-Da-Vida, coécrit, hard rock, heavy metal, sœur, mort, Variety, douche, maison, ami, Bruxelles, Belgique, fan, week-end, série, date, septembre, Twitter, X, décision, tickets vendus, interpréter, rôle, Jackson Maine, country , déclin, proie , problème, alcool, Eddie Vedder, chanteur, attitude, scénique, images, filmé, trio, forme, acolytes , Europe , passage, Beach Festival, Nieuwpoort, nord, américain, en septembre, Portnoy, prog metal, Boston, absence, défi, chaîne, YouTube, Drumeo, Pneumo, album, Fear Inoculum, 2019, percussionniste, maîtriser. --- Classic 21 vous informe des dernières actualités du rock, en Belgique et partout ailleurs. Le Journal du Rock, chaque jour à 7h30 et 18h30. Merci pour votre écoute Pour écouter Classic 21 à tout moment : www.rtbf.be/classic21 Retrouvez tous les contenus de la RTBF sur notre plateforme Auvio.be Et si vous avez apprécié ce podcast, n'hésitez pas à nous donner des étoiles ou des commentaires, cela nous aide à le faire connaître plus largement.
In this FINALE Season Episode, yay! #67 "Kings rule over Tiamat secures the Astar" for Season 7 2023-2024, we return from the last moment where King Mahlon back in Mitsrayim Ancient Egypt giving his speech of progress and blueprint to finding all those responsible what took place leading up to the very first Episode 1: Before The Time Of Princess Aamina of this podcast show! Many world leaders and future leaders such as the young Daniy'el | that will be the husband to Princess Aamina in later years | appeared in this episode as a very young boy of royal with his Ankui father both near the throne of Mitsrayim. We get a glimpse of young Princess Hannah the sister to Princess Aamina (not born yet) and how Princess Hannah is handling the onset returning back to beloved home country of Mitsrayim. | What is the Astar? Why did the Great General Hanee of Orion was in search of it on Tiamat? You have to Get in the Know of prior Episodes and Seasons for some clues :) Does the Astar still stills deep in one of the hidden pyramids overseen and rule control by King Mahon, as the ruler King for intergalatical leadership of Tiamat and Mitsrayim still know where it is? If so, does the whereabouts of the Astar near the portal access and runway of starships that Nahor had access too and in Premiere Show PA1 " Future Series of Nahor and Princess Aamina (PA1) "Entering the Mystics must answer to King Mahlon? Hmmmmm....you to find out by tuning in the collection of MOLIAE Short Stories to get in the know. | Divine Storytelling Is Giving | Later the finale for the first time introduces Kohane in a way that he has a significant scene, he is the stepfather of Dumah ( not born yet and close suppose to be mate with Princess Aamina) in this finale will you found out their mother? In ancient times they called her Istaskal (the mother of Daniy'el and Dumah, as has they had the same mother but different father), say what? yes drama over there) nonetheless, in this FINALE Episode, this moment is important as it sets the path to find who, why two different Ankui of these brothers, Istaskal birth two royal sons. Why Istaskal traveled to Tiamat (Earth) to see King Kohane again and did she informed him for what it seems he was waiting for a long time? What was their deal way in the beginning before the time of Princess Aamina? PowerBREAKERS. PowerMAKERS. **Tune in to find out and if you loving these episodes support the M-Film project by buying the released music and now songs of forthcoming album are now available on the podcast and production official website, where these songs are only available for exclusive access and enjoyment for you and your family. go to: - MOLIAE.com/song | You Know Me | Song and/or go to: -MOLIAE.com/Atlantans-song | Atlantans | Song that at this release of MOLIAE Finale Episode the debuts 5.24 --- You can also support the Dream for the funding of the M-Film project by shopping MOLIAE Merch and following the production on social media: : a) go to moliae.com and buy merch, the music songs remix are out get the exclusive song only thru us : b) follow on social media: IG: MOLIAE8 | YouTube: youtube.com/moliae | Twitter: MOLIAE : c) share this with someone and donate for the dream to make this a feature movie at moliae.com/donation : d) Share this, follow the podcast and tune in as you support the podcast and get you a pick up a MOLIAE NFT at MINT.MOLIAEWORLD.com : e) Get in the Know of MOLIAE go thru the free access of previous seasons to tune in the wonderful world of divine storytelling to be inspired, motivated, and encourage, entertain in each episode, while the podcast after today release of FINALE episode goes on annual summer break from June to August, that returns for a new season in September with the Premiere show returning on 23rd of September 2024. ------ Visit Official Website: MOLIAE.com Season 7 Podcast Episode Release Schedule 2023-2024 ---- **Begins In Season 8 2024-2025 for another year of the Podcast Show Return |Get the PRIME access for the continuation of extra content of Future Series of Nahor and Princess Aamina on Substack Membership subscribe today: Sign up here: https://moliaeworldshortstories.substack.com/ In today wonderful return bonus, we are enthralled of this lovely and beautiful memories of stories that focuses on Egyptian | Mitsrayim born; Nahor , the son of Ezri the Highest Rank Herbalist of Mitsrayim (Ancient Egypt) of the Royal Family and Palace and as well of the whole Sudan and Princes Aamina, the daughter of King Mahlon and Queen Hagar of Mitsrayim. --- | ---- | ---- Visit Official Website: MOLIAE.com Get Nichel MOLIAE song "You Know Me" at: https://MOLIAE.com/Song | from the forthcoming album "When Love Was Divine" --- MOLIAE Music "When Love Was Divine" now available for download as the anticipated wait for the album. --- Buy The Book: "Mitsrayim: A Memoir of A Past Life In Ancient Egypt" Available on Amazon.com and Barnes-n-Noble -- MOLIAE MERCH Tshirts | Support This Podcast Show - Buy Our T-shirts https://moliae.com/shop ------- ANNOUNCEMENTS NFTs Collection "Pyramid Mystery Temple Reunion" PMTR the MINT date is TBA. Visit official website:MOLIAEWorld.com MOLIAE Token is TBA get ready plans to be utilize on official cryptocurrency, NFTs website MOLIAEWorld.com Follow this MOLIAE Project on twitter.com/MOLIAEWorld & share it. PMTR NFTs collection is of 10,000 classmates that are pixel pyramids on Ethereum blockchain with symbolism and meaning with numerology and astrology as our ancient ancestors did so in Sumner and Ancient Egypt/Mitsrayim. In this collection theme purpose, additionally, the mystery to whom took their swords is uptmost concern, in particular, the 24 HUJTA swords (there are more swords assigned to each pyramid) when the call to vote (for whom will be task to solve the mystery that will launch another NFTs future collection "Aspu Legends of Lions" that will be in 3D) follow the storyline that is the bedrock of intrigue written by no other than the prestige Ms. Nichel Anderson creating the official MOLIAE World from her book "Mitsrayim: A Memoir of A Past Life in Ancient Egypt". The PMTR NFTs collection Utilities Portfolio will be announce soon as when the website is available for view soon. --- MOLIAE Comic book series episodes will be announce for the timeline before the podcast series - "The disloyalty amongst the Tribal Leaders" available on Webtoons to catch up on this saga series: Vol 1 "A Deal Was Made In The Cosmos" ---- Check Out Brand of Essential Body Oils: MOLIAE Beauty Shop: https://moliaebeauty.com GIFT BOX KITS | You want to send Ancient Egypt in a beautiful one of a kind present treasure chest. Order our gift box kits and be like a Royal ! You remember the times.. https://moliaebeauty.com/collections/gift-box-kits -- FOLLOW MOLIAE on Social Media & Share this! 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幻冬舎の暗号資産（仮想通貨）/ブロックチェーンなどweb3領域の専門メディア「あたらしい経済 https://www.neweconomy.jp/ 」がおくる、Podcast番組です。 ーーーーー ・バイナンス前CEOに4カ月の実刑判決＝報道 ・カシオ、時計事業の50周年記念NFTに「Astar zkEVM」採用 ・コインベース、ビットコインの「ライトニングネットワーク」導入 ・東京都、デジタル証券（セキュリティトークン）市場拡大促進事業の募集開始 ・米司法省、ビットコイン初期投資家ロジャー・バーを脱税等の罪で刑事告発 ・テザー社がBCI企業Blackrock Neurotechに2億ドル投資、大株主に ーーーーー 【関連リンク】 ニュースの詳細や、アーカイブやその他の記事はこちらから https://www.neweconomy.jp/
Solana launched a mainnet beta update, v1.17.31 to handle network congestion that occurred early last week amid soaring meme coin transactions. Meanwhile, Solana has it's own Saga-style Genesis token and now the team behind the Wilder World metaverse has entered a partnership with technology giant Samsung to bring its multiplayer virtual world to a wider audience through the company's smart TVs.PRE-ORDER Saga 2 NOW! ➜ https://solanamobile.com/refer/paulbarron~This episode is sponsored by Wilder World~Wishlist Wilder World on EPIC Games Store ➜ https://www.wilderworld.com/00:00 Intro00:30 Solana congestion fixed01:20 Solana analytics01:56 MEW returns + TwoLoot03:37 Airdrop incoming - $WUFFI04:20 Jupiter Gets Upgrade04:53 Uniswap raises fees + revenue06:20 Uniswap vs SEC07:54 Coinbase interlocutory appeal09:05 Wilder World x Samsung10:20 Web3 TV Bundle11:00 Samsung Next invested in Playtronix11:45 SuiPlay OX112:17 Playtron x Sui13:50 Samsung Genesis Token14:20 Theta x Samsung16:24 Katy Perry x Theta17:16 Astar Network17:42 Outro#Solana #Ethereum #Samsung~Solana Upgrade + 4th Saga Airdrop!
Sony Bank, the financial arm of the renowned Japanese gaming and entertainment conglomerate, Sony Group, has embarked on a proof-of-concept endeavor to introduce its stablecoin, pegged to fiat currency like Yen.Sony also plans to launch a Sony Bank CONNECT app for NFTs and other web3 entertainment rewards linked to its financial services. ~This episode is sponsored by Tangem~Tangem ➜ https://bit.ly/TangemPBNUse Code: "PBN" for Additional Discounts!00:00 intro00:20 Sponsor: Tangem01:02 Sony Bank Stablecoin01:30 Japanese Stablecoin Regulation01:59 Sony Bank Connect App02:22 BlackRock Securitize03:07 BUIDL + July Launch03:44 Stablecoin Diagram04:24 USDC Circle Lost Japan?04:43 Sony NFT Banking Images05:14 sNFT Platform + Playstation Patent05:59 Jasmy CEO History07:19 $JASMY CEO on Sony Strategy08:30 Astar Network x Sony08:49 Yoki Origins Game x Japan Brands09:41 NFT Utility10:27 Mazda x Astar11:07 Astar CEO: Japan Winning Web313:17 Astar Going Cross-Chain13:49 American Stupidity14:12 outro#Crypto #Sony #playstation ~Sony Stablecoin & NFT Bank Launching!
幻冬舎の暗号資産（仮想通貨）/ブロックチェーンなどweb3領域の専門メディア「あたらしい経済 https://www.neweconomy.jp/ 」がおくる、Podcast番組です。 ーーーーー ・ギャラクシーデジタル、1億ドル規模の暗号資産ファンド立ち上げか＝報道 ・HISが「Astar zkEVM」採用、VTuberのNFTアート発行で ・イングランド銀行、「デジタルサンドボックス」の条件を設定 ・三菱UFJ信託銀、「分散型ID」用いたメタバース空間の活動履歴証明の実証実験 ・米SEC、「イーサリアム現物ETF」の上場申請3件に関するパブリックコメント募集 ・アルゼンチン、VASPの登録制度を導入＝報道 ・ビットコインキャッシュ（BCH）が2度目の半減期に到達 ・セレスティア（TIA）、「Arbitrum Orbit」でBlobstreamサポート開始 ・ヴィタリック、ブログで「ミームコイン」に言及 ーーーーー 【関連リンク】 ニュースの詳細や、アーカイブやその他の記事はこちらから https://www.neweconomy.jp/
幻冬舎のブロックチェーン/暗号資産（仮想通貨）/web3専門メディア「あたらしい経済（New economy）」によるaudible特別番組第48弾の一部試し聴きコンテンツです。全編はアマゾンオーディブル（ https://www.audible.co.jp/podcast/B0CZ3SWBQH ）でお楽しみください。 今回はAstar Foundation COOの石川駿氏にご出演いただきました。石川氏に、「Astar zkEVM」はどんなブロックチェーンなのか？、なぜイーサリアム（Ethereum）のレイヤー2を開発することにしたのか、「Astar zkEVM」と既存アスターチェーンの違い、今後のアスターの展開やエコシステムへの参加方法などついて解説していただきました。 聞き手：あたらしい経済 大津賀新也 収録日：2024年3月19日 その他の音声作品：https://www.neweconomy.jp/features/audible あたらしい経済：https://www.neweconomy.jp/
In this week's episode, we delve into a spectrum of cryptocurrency news and developments, ranging from a $4.1 million scam involving a duo in South Korea to innovative collaborations and regulatory challenges across the globe. We explore a scam where fraudsters promised 70% returns on investments, leading to the arrest of the perpetrators by South Korean police. The episode also touches on Hong Kong officials' warnings about increasingly sophisticated crypto scams, Crossmint and Astar's initiatives to drive Web3 adoption in Japan, challenges faced by Binance in the Philippines and Nigeria, and the quirky world of meme coins reaching new heights. Additionally, we discuss Bitwise's foray into the race for an Ethereum spot ETF, signaling a growing interest in crypto integration into traditional financial systems.________https://substack.com/@dcndailycryptonewsNews Links
幻冬舎の暗号資産（仮想通貨）/ブロックチェーンなどweb3領域の専門メディア「あたらしい経済 https://www.neweconomy.jp/ 」がおくる、Podcast番組です。 ーーーーー ・ビットコイン、米ドル建てで史上最高値まで急上昇するも急落 ・アスター「Astar zkEVM」がメインネットローンチ、初のポリゴン「AggLayer」接続チェーンに ・バイナンス、ナイジェリアナイラ（NGN）取り扱い停止へ ・バイナンス、ソラナ上のミームコイン「Dogwifhat（WIF）」を現物取引で取扱いへ ・BRICS、デジタル通貨とブロックチェーン基盤の決済システム構築へ＝報道 ・テラ創設者ド・クオン、上訴に勝利で米国送致が再度却下 ・ドイツ取引所、デジタル資産取引プラットフォーム「DBDX」ローンチ ・ソラナ基盤のDePINプロジェクト「io.net」が約45億円の資金調達実施、ソラナラボやアプトスラボなど参加 ・DEAと東京電力らがDePINコンテンツの実証試験へ、インフラ企業の課題解決の検討で ーーーーー 【関連リンク】 ニュースの詳細や、アーカイブやその他の記事はこちらから https://www.neweconomy.jp/
En este episodio les comparto información de tres proyectos de Polkadot muy interesantes! Astar, Aleph Zero & Hydra DX Links: https://astar.network/astar2 https://alephzero.org/ https://hydradx.io/ Host: Diego Towers Edición: Emanuel Arias Voice Over: Yanina Alba Instagram: https://www.instagram.com/bitcoin_para_todos_/ Youtube: https://www.youtube.com/channel/UCaHX81nmxyVYqh8YEKo4jAg Telegram Bitcoin para todxs: https://t.me/joinchat/GukzYtOdC1dlOTkx Twitter: https://twitter.com/Diego_Torres_ --- Send in a voice message: https://podcasters.spotify.com/pod/show/diego-torres3/message
幻冬舎の暗号資産（仮想通貨）/ブロックチェーンなどweb3領域の専門メディア「あたらしい経済 https://www.neweconomy.jp/ 」がおくる、Podcast番組です。 ーーーーー ・デロイトトーマツ、「Astar zkEVM」採用で野球テーマのNFTゲーム構築へ ・DeFi債券市場「Secured Finance」がアービトラム・アバランチに続きポリゴンzkEVMでローンチ、マルチチェーン対応へ ・イーサリアム大型アップグレード「Dencun」、メインネット実装目標日は3月13日 ・ビットフライヤー、ハッシュパレット「エルフトークン（ELF）」のIEO実施へ。国内5例目 ・クロスチェーンプロトコル「Wormhole」、独自トークン「W」のトークノミクス計画発表 ・バイナンスローンチプール、「Pixels（PIXEL）」取扱へ ・ソラナのオラクルネットワーク「Pyth Network」、エアドロッププログラム第2フェーズ開始 ・クラーケン、オランダで暗号資産事業者の登録完了 ーーーーー 【関連リンク】 ニュースの詳細や、アーカイブやその他の記事はこちらから https://www.neweconomy.jp/
Today I am speaking with Uri Kolodny, Co-Founder at StarkWare, the pioneering team behind Starknet, a Layer 2 solution designed to tackle Ethereum's scalability challenges. Starknet is a Validity Rollup that offers boundless scalability while upholding Ethereum's security and decentralization principles.It's important to note that Uri has been serving as StarkWare's CEO, though he recently shared his decision to step down from this role, as mentioned in his public statement on social media and other platforms. I'd like to clarify that this interview with Uri was conducted prior to his announcement. During our conversation, Uri displayed remarkable kindness and thoughtfulness, and I extend my best wishes to him and his family during this time.My motivation for this interview stems from the fact that, much like last week's conversation with Maarten Henseksns at Astar, Starknet is actively pursuing support on The Graph Network through the Chain Integration Process. Similar to Astar, support for Starknet is expected sometime soon!This interview proved to be both insightful and enjoyable. Uri's brilliance shines through as we explore topics such as entrepreneurship, the significance of Layer 2 solutions, an in-depth understanding of Starknet and its distinctive features, and, of course, the eagerly anticipated integration with The Graph.Show Notes and TranscriptsThe GRTiQ Podcast takes listeners inside web3 and The Graph (GRT) by interviewing members of the ecosystem. Please help support this project and build the community by subscribing and leaving a review.Twitter: GRT_iQwww.GRTiQ.com
Today I am speaking with Maarten Henskens, Head at the Astar Foundation, Japan's leading blockchain, renowned for its robust support for EVM, Substrate, WebAssembly, and ink! environments, which collectively form a scalable, cross-layer, and cross-machine protocol.Exciting news awaits as Astar has embarked on a journey using The Graph's cutting-edge Chain Integration Process to seek integration with the network. The highly anticipated announcement of full support on The Graph is on the horizon. Astar is poised to become the second chain, following Optimism, to join The Graph via this innovative integration process, with more chains eagerly following suit.During our conversation, Maarten will unveil his personal voyage into the realm of web3, which commenced with a career in primary education. We'll delve into his passions, including biking and wine, and discover his lifelong dedication to the pursuit of knowledge. Maarten will then introduce us to the vibrant Astar network and its thriving community, shedding light on what sets Astar apart and the compelling narrative behind their decision to seek support for their chain within The Graph Network.Show Notes and TranscriptsThe GRTiQ Podcast takes listeners inside web3 and The Graph (GRT) by interviewing members of the ecosystem. Please help support this project and build the community by subscribing and leaving a review.Twitter: GRT_iQwww.GRTiQ.com
We are running an end of year survey for our listeners. Let us know any feedback you have for us, what episodes resonated with you the most, and guest requests for 2024! RAG has emerged as one of the key pieces of the AI Engineer stack. Jerry from LlamaIndex called it a “hack”, Bryan from Hex compared it to “a recommendation system from LLMs”, and even LangChain started with it. RAG is crucial in any AI coding workflow. We talked about context quality for code in our Phind episode. Today's guests, Beyang Liu and Steve Yegge from SourceGraph, have been focused on code indexing and retrieval for over 15 years. We locked them in our new studio to record a 1.5 hours masterclass on the history of code search, retrieval interfaces for code, and how they get SOTA 30% completion acceptance rate in their Cody product by being better at the “bin packing problem” of LLM context generation. Google Grok → SourceGraph → CodyWhile at Google in 2008, Steve built Grok, which lives on today as Google Kythe. It allowed engineers to do code parsing and searching across different codebases and programming languages. (You might remember this blog post from Steve's time at Google) Beyang was an intern at Google at the same time, and Grok became the inspiration to start SourceGraph in 2013. The two didn't know eachother personally until Beyang brought Steve out of retirement 9 years later to join him as VP Engineering. Fast forward 10 years, SourceGraph has become to best code search tool out there and raised $223M along the way. Nine months ago, they open sourced SourceGraph Cody, their AI coding assistant. All their code indexing and search infrastructure allows them to get SOTA results by having better RAG than competitors:* Code completions as you type that achieve an industry-best Completion Acceptance Rate (CAR) as high as 30% using a context-enhanced open-source LLM (StarCoder)* Context-aware chat that provides the option of using GPT-4 Turbo, Claude 2, GPT-3.5 Turbo, Mistral 7x8B, or Claude Instant, with more model integrations planned* Doc and unit test generation, along with AI quick fixes for common coding errors* AI-enhanced natural language code search, powered by a hybrid dense/sparse vector search engine There are a few pieces of infrastructure that helped Cody achieve these results:Dense-sparse vector retrieval system For many people, RAG = vector similarity search, but there's a lot more that you can do to get the best possible results. From their release:"Sparse vector search" is a fancy name for keyword search that potentially incorporates LLMs for things like ranking and term expansion (e.g., "k8s" expands to "Kubernetes container orchestration", possibly weighted as in SPLADE): * Dense vector retrieval makes use of embeddings, the internal representation that LLMs use to represent text. Dense vector retrieval provides recall over a broader set of results that may have no exact keyword matches but are still semantically similar. * Sparse vector retrieval is very fast, human-understandable, and yields high recall of results that closely match the user query. * We've found the approaches to be complementary.There's a very good blog post by Pinecone on SPLADE for sparse vector search if you're interested in diving in. If you're building RAG applications in areas that have a lot of industry-specific nomenclature, acronyms, etc, this is a good approach to getting better results.SCIPIn 2016, Microsoft announced the Language Server Protocol (LSP) and the Language Server Index Format (LSIF). This protocol makes it easy for IDEs to get all the context they need from a codebase to get things like file search, references, “go to definition”, etc. SourceGraph developed SCIP, “a better code indexing format than LSIF”:* Simpler and More Efficient Format: SCIP utilizes Protobuf instead of JSON, which is used by LSIF. Protobuf is more space-efficient, simpler, and more suitable for systems programming. * Better Performance and Smaller Index Sizes: SCIP indexers, such as scip-clang, show enhanced performance and reduced index file sizes compared to LSIF indexers (10%-20% smaller)* Easier to Develop and Debug: SCIP's design, centered around human-readable string IDs for symbols, makes it faster and more straightforward to develop new language indexers. Having more efficient indexing is key to more performant RAG on code. Show Notes* Sourcegraph* Cody* Copilot vs Cody* Steve's Stanford seminar on Grok* Steve's blog* Grab* Fireworks* Peter Norvig* Noam Chomsky* Code search* Kelly Norton* Zoekt* v0.devSee also our past episodes on Cursor, Phind, Codeium and Codium as well as the GitHub Copilot keynote at AI Engineer Summit.Timestamps* [00:00:00] Intros & Backgrounds* [00:05:20] How Steve's work on Grok inspired SourceGraph for Beyang* [00:08:10] What's Cody?* [00:11:22] Comparison of coding assistants and the capabilities of Cody* [00:16:00] The importance of context (RAG) in AI coding tools* [00:21:33] The debate between Chomsky and Norvig approaches in AI* [00:30:06] Normsky: the Norvig + Chomsky models collision* [00:36:00] The death of the DSL?* [00:40:00] LSP, Skip, Kythe, BFG, and all that fun stuff* [00:53:00] The SourceGraph internal stack* [00:58:46] Building on open source models* [01:02:00] SourceGraph for engineering managers?* [01:12:00] Lightning RoundTranscriptAlessio: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:16]Swyx: Hey, and today we're christening our new podcast studio in the Newton, and we have Beyang and Steve from Sourcegraph. Welcome. [00:00:25]Beyang: Hey, thanks for having us. [00:00:26]Swyx: So this has been a long time coming. I'm very excited to have you. We also are just celebrating the one year anniversary of ChatGPT yesterday, but also we'll be talking about the GA of Cody later on today. We'll just do a quick intros of both of you. Obviously, people can research you and check the show notes for more. Beyang, you worked in computer vision at Stanford and then you worked at Palantir. I did, yeah. You also interned at Google. [00:00:48]Beyang: I did back in the day where I get to use Steve's system, DevTool. [00:00:53]Swyx: Right. What was it called? [00:00:55]Beyang: It was called Grok. Well, the end user thing was Google Code Search. That's what everyone called it, or just like CS. But the brains of it were really the kind of like Trigram index and then Grok, which provided the reference graph. [00:01:07]Steve: Today it's called Kythe, the open source Google one. It's sort of like Grok v3. [00:01:11]Swyx: On your podcast, which you've had me on, you've interviewed a bunch of other code search developers, including the current developer of Kythe, right? [00:01:19]Beyang: No, we didn't have any Kythe people on, although we would love to if they're up for it. We had Kelly Norton, who built a similar system at Etsy, it's an open source project called Hound. We also had Han-Wen Nienhuys, who created Zoekt, which is, I think, heavily inspired by the Trigram index that powered Google's original code search and that we also now use at Sourcegraph. Yeah. [00:01:45]Swyx: So you teamed up with Quinn over 10 years ago to start Sourcegraph and you were indexing all code on the internet. And now you're in a perfect spot to create a code intelligence startup. Yeah, yeah. [00:01:56]Beyang: I guess the backstory was, I used Google Code Search while I was an intern. And then after I left that internship and worked elsewhere, it was the single dev tool that I missed the most. I felt like my job was just a lot more tedious and much more of a hassle without it. And so when Quinn and I started working together at Palantir, he had also used various code search engines in open source over the years. And it was just a pain point that we both felt, both working on code at Palantir and also working within Palantir's clients, which were a lot of Fortune 500 companies, large financial institutions, folks like that. And if anything, the pains they felt in dealing with large complex code bases made our pain points feel small by comparison. So that was really the impetus for starting Sourcegraph. [00:02:42]Swyx: Yeah, excellent. Steve, you famously worked at Amazon. And you've told many, many stories. I want every single listener of Latent Space to check out Steve's YouTube because he effectively had a podcast that you didn't tell anyone about or something. You just hit record and just went on a few rants. I'm always here for your Stevie rants. And then you moved to Google, where you also had some interesting thoughts on just the overall Google culture versus Amazon. You joined Grab as head of eng for a couple of years. I'm from Singapore, so I have actually personally used a lot of Grab's features. And it was very interesting to see you talk so highly of Grab's engineering and sort of overall prospects. [00:03:21]Steve: Because as a customer, it sucked? [00:03:22]Swyx: Yeah, no, it's just like, being from a smaller country, you never see anyone from our home country being on a global stage or talked about as a startup that people admire or look up to, like on the league that you, with all your legendary experience, would consider equivalent. Yeah. [00:03:41]Steve: Yeah, no, absolutely. They actually, they didn't even know that they were as good as they were, in a sense. They started hiring a bunch of people from Silicon Valley to come in and sort of like fix it. And we came in and we were like, Oh, we could have been a little better operational excellence and stuff. But by and large, they're really sharp. The only thing about Grab is that they get criticized a lot for being too westernized. Oh, by who? By Singaporeans who don't want to work there. [00:04:06]Swyx: Okay. I guess I'm biased because I'm here, but I don't see that as a problem. If anything, they've had their success because they were more westernized than the Sanders Singaporean tech company. [00:04:15]Steve: I mean, they had their success because they are laser focused. They copy to Amazon. I mean, they're executing really, really, really well for a giant. I was on a slack with 2,500 engineers. It was like this giant waterfall that you could dip your toe into. You'd never catch up. Actually, the AI summarizers would have been really helpful there. But yeah, no, I think Grab is successful because they're just out there with their sleeves rolled up, just making it happen. [00:04:43]Swyx: And for those who don't know, it's not just like Uber of Southeast Asia, it's also a super app. PayPal Plus. [00:04:48]Steve: Yeah. [00:04:49]Swyx: In the way that super apps don't exist in the West. It's one of the enduring mysteries of B2C that super apps work in the East and don't work in the West. We just don't understand it. [00:04:57]Beyang: Yeah. [00:04:58]Steve: It's just kind of curious. They didn't work in India either. And it was primarily because of bandwidth reasons and smaller phones. [00:05:03]Swyx: That should change now. It should. [00:05:05]Steve: And maybe we'll see a super app here. [00:05:08]Swyx: You retired-ish? I did. You retired-ish on your own video game? Mm-hmm. Any fun stories about that? And that's also where you discovered some need for code search, right? Mm-hmm. [00:05:16]Steve: Sure. A need for a lot of stuff. Better programming languages, better databases. Better everything. I mean, I started in like 95, right? Where there was kind of nothing. Yeah. Yeah. [00:05:24]Beyang: I just want to say, I remember when you first went to Grab because you wrote that blog post talking about why you were excited about it, about like the expanding Asian market. And our reaction was like, oh, man, how did we miss stealing it with you? [00:05:36]Swyx: Hiring you. [00:05:37]Beyang: Yeah. [00:05:38]Steve: I was like, miss that. [00:05:39]Swyx: Tell that story. So how did this happen? Right? So you were inspired by Grok. [00:05:44]Beyang: I guess the backstory from my point of view is I had used code search and Grok while at Google, but I didn't actually know that it was connected to you, Steve. I knew you from your blog posts, which were always excellent, kind of like inside, very thoughtful takes from an engineer's perspective on some of the challenges facing tech companies and tech culture and that sort of thing. But my first introduction to you within the context of code intelligence, code understanding was I watched a talk that you gave, I think at Stanford, about Grok when you're first building it. And that was very eye opening. I was like, oh, like that guy, like the guy who, you know, writes the extremely thoughtful ranty like blog posts also built that system. And so that's how I knew, you know, you were involved in that. And then, you know, we always wanted to hire you, but never knew quite how to approach you or, you know, get that conversation started. [00:06:34]Steve: Well, we got introduced by Max, right? Yeah. It was temporal. Yeah. Yeah. I mean, it was a no brainer. They called me up and I had noticed when Sourcegraph had come out. Of course, when they first came out, I had this dagger of jealousy stabbed through me piercingly, which I remember because I am not a jealous person by any means, ever. But boy, I was like, but I was kind of busy, right? And just one thing led to another. I got sucked back into the ads vortex and whatever. So thank God Sourcegraph actually kind of rescued me. [00:07:05]Swyx: Here's a chance to build DevTools. Yeah. [00:07:08]Steve: That's the best. DevTools are the best. [00:07:10]Swyx: Cool. Well, so that's the overall intro. I guess we can get into Cody. Is there anything else that like people should know about you before we get started? [00:07:18]Steve: I mean, everybody knows I'm a musician. I can juggle five balls. [00:07:24]Swyx: Five is good. Five is good. I've only ever managed three. [00:07:27]Steve: Five is hard. Yeah. And six, a little bit. [00:07:30]Swyx: Wow. [00:07:31]Beyang: That's impressive. [00:07:32]Alessio: So yeah, to jump into Sourcegraph, this has been a company 10 years in the making. And as Sean said, now you're at the right place. Phase two. Now, exactly. You spent 10 years collecting all this code, indexing, making it easy to surface it. Yeah. [00:07:47]Swyx: And also learning how to work with enterprises and having them trust you with their code bases. Yeah. [00:07:52]Alessio: Because initially you were only doing on-prem, right? Like a lot of like VPC deployments. [00:07:55]Beyang: So in the very early days, we're cloud only. But the first major customers we landed were all on-prem, self-hosted. And that was, I think, related to the nature of the problem that we're solving, which becomes just like a critical, unignorable pain point once you're above like 100 devs or so. [00:08:11]Alessio: Yeah. And now Cody is going to be GA by the time this releases. So congrats to your future self for launching this in two weeks. Can you give a quick overview of just what Cody is? I think everybody understands that it's a AI coding agent, but a lot of companies say they have a AI coding agent. So yeah, what does Cody do? How do people interface with it? [00:08:32]Beyang: Yeah. So how is it different from the like several dozen other AI coding agents that exist in the market now? When we thought about building a coding assistant that would do things like code generation and question answering about your code base, I think we came at it from the perspective of, you know, we've spent the past decade building the world's best code understanding engine for human developers, right? So like it's kind of your guide as a human dev if you want to go and dive into a large complex code base. And so our intuition was that a lot of the context that we're providing to human developers would also be useful context for AI developers to consume. And so in terms of the feature set, Cody is very similar to a lot of other assistants. It does inline autocompletion. It does code base aware chat. It does specific commands that automate, you know, tasks that you might rather not want to do like generating unit tests or adding detailed documentation. But we think the core differentiator is really the quality of the context, which is hard to kind of describe succinctly. It's a bit like saying, you know, what's the difference between Google and Alta Vista? There's not like a quick checkbox list of features that you can rattle off, but it really just comes down to all the attention and detail that we've paid to making that context work well and be high quality and fast for human devs. We're now kind of plugging into the AI coding assistant as well. Yeah. [00:09:53]Steve: I mean, just to add my own perspective on to what Beyang just described, RAG is kind of like a consultant that the LLM has available, right, that knows about your code. RAG provides basically a bridge to a lookup system for the LLM, right? Whereas fine tuning would be more like on the job training for somebody. If the LLM is a person, you know, and you send them to a new job and you do on the job training, that's what fine tuning is like, right? So tuned to our specific task. You're always going to need that expert, even if you get the on the job training, because the expert knows your particular code base, your task, right? That expert has to know your code. And there's a chicken and egg problem because, right, you know, we're like, well, I'm going to ask the LLM about my code, but first I have to explain it, right? It's this chicken and egg problem. That's where RAG comes in. And we have the best consultants, right? The best assistant who knows your code. And so when you sit down with Cody, right, what Beyang said earlier about going to Google and using code search and then starting to feel like without it, his job was super tedious. Once you start using these, do you guys use coding assistants? [00:10:53]Swyx: Yeah, right. [00:10:54]Steve: I mean, like we're getting to the point very quickly, right? Where you feel like almost like you're programming without the internet, right? Or something, you know, it's like you're programming back in the nineties without the coding assistant. Yeah. Hopefully that helps for people who have like no idea about coding systems, what they are. [00:11:09]Swyx: Yeah. [00:11:10]Alessio: I mean, going back to using them, we had a lot of them on the podcast already. We had Cursor, we have Codium and Codium, very similar names. [00:11:18]Swyx: Yeah. Find, and then of course there's Copilot. [00:11:22]Alessio: You had a Copilot versus Cody blog post, and I think it really shows the context improvement. So you had two examples that stuck with me. One was, what does this application do? And the Copilot answer was like, oh, it uses JavaScript and NPM and this. And it's like, but that's not what it does. You know, that's what it's built with. Versus Cody was like, oh, these are like the major functions. And like, these are the functionalities and things like that. And then the other one was, how do I start this up? And Copilot just said NPM start, even though there was like no start command in the package JSON, but you know, most collapse, right? Most projects use NPM start. So maybe this does too. How do you think about open source models? Because Copilot has their own private thing. And I think you guys use Starcoder, if I remember right. Yeah, that's correct. [00:12:09]Beyang: I think Copilot uses some variant of Codex. They're kind of cagey about it. I don't think they've like officially announced what model they use. [00:12:16]Swyx: And I think they use a range of models based on what you're doing. Yeah. [00:12:19]Beyang: So everyone uses a range of model. Like no one uses the same model for like inline completion versus like chat because the latency requirements for. Oh, okay. Well, there's fill in the middle. There's also like what the model's trained on. So like we actually had completions powered by Claude Instant for a while. And but you had to kind of like prompt hack your way to get it to output just the code and not like, hey, you know, here's the code you asked for, like that sort of text. So like everyone uses a range of models. We've kind of designed Cody to be like especially model, not agnostic, but like pluggable. So one of our kind of design considerations was like as the ecosystem evolves, we want to be able to integrate the best in class models, whether they're proprietary or open source into Cody because the pace of innovation in the space is just so quick. And I think that's been to our advantage. Like today, Cody uses Starcoder for inline completions. And with the benefit of the context that we provide, we actually show comparable completion acceptance rate metrics. It's kind of like the standard metric that folks use to evaluate inline completion quality. It's like if I show you a completion, what's the chance that you actually accept the completion versus you reject it? And so we're at par with Copilot, which is at the head of that industry right now. And we've been able to do that with the Starcoder model, which is open source and the benefit of the context fetching stuff that we provide. And of course, a lot of like prompt engineering and other stuff along the way. [00:13:40]Alessio: And Steve, you wrote a post called cheating is all you need about what you're building. And one of the points you made is that everybody's fighting on the same axis, which is better UI and the IDE, maybe like a better chat response. But data modes are kind of the most important thing. And you guys have like a 10 year old mode with all the data you've been collecting. How do you kind of think about what other companies are doing wrong, right? Like, why is nobody doing this in terms of like really focusing on RAG? I feel like you see so many people. Oh, we just got a new model. It's like a bit human eval. And it's like, well, but maybe like that's not what we should really be doing, you know? Like, do you think most people underestimate the importance of like the actual RAG in code? [00:14:21]Steve: I think that people weren't doing it much. It wasn't. It's kind of at the edges of AI. It's not in the center. I know that when ChatGPT launched, so within the last year, I've heard a lot of rumblings from inside of Google, right? Because they're undergoing a huge transformation to try to, you know, of course, get into the new world. And I heard that they told, you know, a bunch of teams to go and train their own models or fine tune their own models, right? [00:14:43]Swyx: Both. [00:14:43]Steve: And, you know, it was a s**t show. Nobody knew how to do it. They launched two coding assistants. One was called Code D with an EY. And then there was, I don't know what happened in that one. And then there's Duet, right? Google loves to compete with themselves, right? They do this all the time. And they had a paper on Duet like from a year ago. And they were doing exactly what Copilot was doing, which was just pulling in the local context, right? But fundamentally, I thought of this because we were talking about the splitting of the [00:15:10]Swyx: models. [00:15:10]Steve: In the early days, it was the LLM did everything. And then we realized that for certain use cases, like completions, that a different, smaller, faster model would be better. And that fragmentation of models, actually, we expected to continue and proliferate, right? Because we are fundamentally, we're a recommender engine right now. Yeah, we're recommending code to the LLM. We're saying, may I interest you in this code right here so that you can answer my question? [00:15:34]Swyx: Yeah? [00:15:34]Steve: And being good at recommender engine, I mean, who are the best recommenders, right? There's YouTube and Spotify and, you know, Amazon or whatever, right? Yeah. [00:15:41]Swyx: Yeah. [00:15:41]Steve: And they all have many, many, many, many, many models, right? For all fine-tuned for very specific, you know. And that's where we're heading in code, too. Absolutely. [00:15:50]Swyx: Yeah. [00:15:50]Alessio: We just did an episode we released on Wednesday, which we said RAG is like Rexis or like LLMs. You're basically just suggesting good content. [00:15:58]Swyx: It's like what? Recommendations. [00:15:59]Beyang: Recommendations. [00:16:00]Alessio: Oh, got it. [00:16:01]Steve: Yeah, yeah, yeah. [00:16:02]Swyx: So like the naive implementation of RAG is you embed everything, throw it in a vector database, you embed your query, and then you find the nearest neighbors, and that's your RAG. But actually, you need to rank it. And actually, you need to make sure there's sample diversity and that kind of stuff. And then you're like slowly gradient dissenting yourself towards rediscovering proper Rexis, which has been traditional ML for a long time. But like approaching it from an LLM perspective. Yeah. [00:16:24]Beyang: I almost think of it as like a generalized search problem because it's a lot of the same things. Like you want your layer one to have high recall and get all the potential things that could be relevant. And then there's typically like a layer two re-ranking mechanism that bumps up the precision and tries to get the relevant stuff to the top of the results list. [00:16:43]Swyx: Have you discovered that ranking matters a lot? Oh, yeah. So the context is that I think a lot of research shows that like one, context utilization matters based on model. Like GPT uses the top of the context window, and then apparently Claude uses the bottom better. And it's lossy in the middle. Yeah. So ranking matters. No, it really does. [00:17:01]Beyang: The skill with which models are able to take advantage of context is always going to be dependent on how that factors into the impact on the training loss. [00:17:10]Swyx: Right? [00:17:10]Beyang: So like if you want long context window models to work well, then you have to have a ton of data where it's like, here's like a billion lines of text. And I'm going to ask a question about like something that's like, you know, embedded deeply into it and like, give me the right answer. And unless you have that training set, then of course, you're going to have variability in terms of like where it attends to. And in most kind of like naturally occurring data, the thing that you're talking about right now, the thing I'm asking you about is going to be something that we talked about recently. [00:17:36]Swyx: Yeah. [00:17:36]Steve: Did you really just say gradient dissenting yourself? Actually, I love that it's entered the casual lexicon. Yeah, yeah, yeah. [00:17:44]Swyx: My favorite version of that is, you know, how we have to p-hack papers. So, you know, when you throw humans at the problem, that's called graduate student dissent. That's great. It's really awesome. [00:17:54]Alessio: I think the other interesting thing that you have is this inline assist UX that I wouldn't say async, but like it works while you can also do work. So you can ask Cody to make changes on a code block and you can still edit the same file at the same time. [00:18:07]Swyx: Yeah. [00:18:07]Alessio: How do you see that in the future? Like, do you see a lot of Cody's running together at the same time? Like, how do you validate also that they're not messing each other up as they make changes in the code? And maybe what are the limitations today? And what do you think about where the attack is going? [00:18:21]Steve: I want to start with a little history and then I'm going to turn it over to Bian, all right? So we actually had this feature in the very first launch back in June. Dominic wrote it. It was called nonstop Cody. And you could have multiple, basically, LLM requests in parallel modifying your source [00:18:37]Swyx: file. [00:18:37]Steve: And he wrote a bunch of code to handle all of the diffing logic. And you could see the regions of code that the LLM was going to change, right? And he was showing me demos of it. And it just felt like it was just a little before its time, you know? But a bunch of that stuff, that scaffolding was able to be reused for where we're inline [00:18:56]Swyx: sitting today. [00:18:56]Steve: How would you characterize it today? [00:18:58]Beyang: Yeah, so that interface has really evolved from a, like, hey, general purpose, like, request anything inline in the code and have the code update to really, like, targeted features, like, you know, fix the bug that exists at this line or request a very specific [00:19:13]Swyx: change. [00:19:13]Beyang: And the reason for that is, I think, the challenge that we ran into with inline fixes, and we do want to get to the point where you could just fire and forget and have, you know, half a dozen of these running in parallel. But I think we ran into the challenge early on that a lot of people are running into now when they're trying to construct agents, which is the reliability of, you know, working code generation is just not quite there yet in today's language models. And so that kind of constrains you to an interaction where the human is always, like, in the inner loop, like, checking the output of each response. And if you want that to work in a way where you can be asynchronous, you kind of have to constrain it to a domain where today's language models can generate reliable code well enough. So, you know, generating unit tests, that's, like, a well-constrained problem. Or fixing a bug that shows up as, like, a compiler error or a test error, that's a well-constrained problem. But the more general, like, hey, write me this class that does X, Y, and Z using the libraries that I have, that is not quite there yet, even with the benefit of really good context. Like, it definitely moves the needle a lot, but we're not quite there yet to the point where you can just fire and forget. And I actually think that this is something that people don't broadly appreciate yet, because I think that, like, everyone's chasing this dream of agentic execution. And if we're to really define that down, I think it implies a couple things. You have, like, a multi-step process where each step is fully automated. We don't have to have a human in the loop every time. And there's also kind of like an LM call at each stage or nearly every stage in that [00:20:45]Swyx: chain. [00:20:45]Beyang: Based on all the work that we've done, you know, with the inline interactions, with kind of like general Codyfeatures for implementing longer chains of thought, we're actually a little bit more bearish than the average, you know, AI hypefluencer out there on the feasibility of agents with purely kind of like transformer-based models. To your original question, like, the inline interactions with CODI, we actually constrained it to be more targeted, like, you know, fix the current error or make this quick fix. I think that that does differentiate us from a lot of the other tools on the market, because a lot of people are going after this, like, shnazzy, like, inline edit interaction, whereas I think where we've moved, and this is based on the user feedback that we've gotten, it's like that sort of thing, it demos well, but when you're actually coding day to day, you don't want to have, like, a long chat conversation inline with the code base. That's a waste of time. You'd rather just have it write the right thing and then move on with your life or not have to think about it. And that's what we're trying to work towards. [00:21:37]Steve: I mean, yeah, we're not going in the agent direction, right? I mean, I'll believe in agents when somebody shows me one that works. Yeah. Instead, we're working on, you know, sort of solidifying our strength, which is bringing the right context in. So new context sources, ways for you to plug in your own context, ways for you to control or influence the context, you know, the mixing that happens before the request goes out, etc. And there's just so much low-hanging fruit left in that space that, you know, agents seems like a little bit of a boondoggle. [00:22:03]Beyang: Just to dive into that a little bit further, like, I think, you know, at a very high level, what do people mean when they say agents? They really mean, like, greater automation, fully automated, like, the dream is, like, here's an issue, go implement that. And I don't have to think about it as a human. And I think we are working towards that. Like, that is the eventual goal. I think it's specifically the approach of, like, hey, can we have a transformer-based LM alone be the kind of, like, backbone or the orchestrator of these agentic flows? Where we're a little bit more bearish today. [00:22:31]Swyx: You want the human in the loop. [00:22:32]Beyang: I mean, you kind of have to. It's just a reality of the behavior of language models that are purely, like, transformer-based. And I think that's just like a reflection of reality. And I don't think people realize that yet. Because if you look at the way that a lot of other AI tools have implemented context fetching, for instance, like, you see this in the Copilot approach, where if you use, like, the at-workspace thing that supposedly provides, like, code-based level context, it has, like, an agentic approach where you kind of look at how it's behaving. And it feels like they're making multiple requests to the LM being like, what would you do in this case? Would you search for stuff? What sort of files would you gather? Go and read those files. And it's like a multi-hop step, so it takes a long while. It's also non-deterministic. Because any sort of, like, LM invocation, it's like a dice roll. And then at the end of the day, the context it fetches is not that good. Whereas our approach is just like, OK, let's do some code searches that make sense. And then maybe, like, crawl through the reference graph a little bit. That is fast. That doesn't require any sort of LM invocation at all. And we can pull in much better context, you know, very quickly. So it's faster. [00:23:37]Swyx: It's more reliable. [00:23:37]Beyang: It's deterministic. And it yields better context quality. And so that's what we think. We just don't think you should cargo cult or naively go like, you know, agents are the [00:23:46]Swyx: future. [00:23:46]Beyang: Let's just try to, like, implement agents on top of the LM that exists today. I think there are a couple of other technologies or approaches that need to be refined first before we can get into these kind of, like, multi-stage, fully automated workflows. [00:24:00]Swyx: It makes sense. You know, we're very much focused on developer inner loop right now. But you do see things eventually moving towards developer outer loop. Yeah. So would you basically say that they're tackling the agent's problem that you don't want to tackle? [00:24:11]Beyang: No, I would say at a high level, we are after maybe, like, the same high level problem, which is like, hey, I want some code written. I want to develop some software and can automate a system. Go build that software for me. I think the approaches might be different. So I think the analogy in my mind is, I think about, like, the AI chess players. Coding, in some senses, I mean, it's similar and dissimilar to chess. I think one question I ask is, like, do you think producing code is more difficult than playing chess or less difficult than playing chess? More. [00:24:41]Swyx: I think more. [00:24:41]Beyang: Right. And if you look at the best AI chess players, like, yes, you can use an LLM to play chess. Like, people have showed demos where it's like, oh, like, yeah, GPT-4 is actually a pretty decent, like, chess move suggester. Right. But you would never build, like, a best in class chess player off of GPT-4 alone. [00:24:57]Swyx: Right. [00:24:57]Beyang: Like, the way that people design chess players is that you have kind of like a search space and then you have a way to explore that search space efficiently. There's a bunch of search algorithms, essentially. We were doing tree search in various ways. And you can have heuristic functions, which might be powered by an LLM. [00:25:12]Swyx: Right. [00:25:12]Beyang: Like, you might use an LLM to generate proposals in that space that you can efficiently explore. But the backbone is still this kind of more formalized tree search based approach rather than the LLM itself. And so I think my high level intuition is that, like, the way that we get to more reliable multi-step workflows that do things beyond, you know, generate unit test, it's really going to be like a search based approach where you use an LLM as kind of like an advisor or a proposal function, sort of your heuristic function, like the ASTAR search algorithm. But it's probably not going to be the thing that is the backbone, because I guess it's not the right tool for that. Yeah. [00:25:50]Swyx: I can see yourself kind of thinking through this, but not saying the words, the sort of philosophical Peter Norvig type discussion. Maybe you want to sort of introduce that in software. Yeah, definitely. [00:25:59]Beyang: So your listeners are savvy. They're probably familiar with the classic like Chomsky versus Norvig debate. [00:26:04]Swyx: No, actually, I wanted, I was prompting you to introduce that. Oh, got it. [00:26:08]Beyang: So, I mean, if you look at the history of artificial intelligence, right, you know, it goes way back to, I don't know, it's probably as old as modern computers, like 50s, 60s, 70s. People are debating on like, what is the path to producing a sort of like general human level of intelligence? And kind of two schools of thought that emerged. One is the Norvig school of thought, which roughly speaking includes large language models, you know, regression, SVN, basically any model that you kind of like learn from data. And it's like data driven. Most of machine learning would fall under this umbrella. And that school of thought says like, you know, just learn from the data. That's the approach to reaching intelligence. And then the Chomsky approach is more things like compilers and parsers and formal systems. So basically like, let's think very carefully about how to construct a formal, precise system. And that will be the approach to how we build a truly intelligent system. I think Lisp was invented so that you could create like rules-based systems that you would call AI. As a language. Yeah. And for a long time, there was like this debate, like there's certain like AI research labs that were more like, you know, in the Chomsky camp and others that were more in the Norvig camp. It's a debate that rages on today. And I feel like the consensus right now is that, you know, Norvig definitely has the upper hand right now with the advent of LMs and diffusion models and all the other recent progress in machine learning. But the Chomsky-based stuff is still really useful in my view. I mean, it's like parsers, compilers, basically a lot of the stuff that provides really good context. It provides kind of like the knowledge graph backbone that you want to explore with your AI dev tool. Like that will come from kind of like Chomsky-based tools like compilers and parsers. It's a lot of what we've invested in in the past decade at Sourcegraph and what you build with Grok. Basically like these formal systems that construct these very precise knowledge graphs that are great context providers and great kind of guard rails enforcers and kind of like safety checkers for the output of a more kind of like data-driven, fuzzier system that uses like the Norvig-based models. [00:28:03]Steve: Jang was talking about this stuff like it happened in the middle ages. Like, okay, so when I was in college, I was in college learning Lisp and prologue and planning and all the deterministic Chomsky approaches to AI. And I was there when Norvig basically declared it dead. I was there 3,000 years ago when Norvig and Chomsky fought on the volcano. When did he declare it dead? [00:28:26]Swyx: What do you mean he declared it dead? [00:28:27]Steve: It was like late 90s. [00:28:29]Swyx: Yeah. [00:28:29]Steve: When I went to Google, Peter Norvig was already there. He had basically like, I forget exactly where. It was some, he's got so many famous short posts, you know, amazing. [00:28:38]Swyx: He had a famous talk, the unreasonable effectiveness of data. Yeah. [00:28:41]Steve: Maybe that was it. But at some point, basically, he basically convinced everybody that deterministic approaches had failed and that heuristic-based, you know, data-driven statistical approaches, stochastic were better. [00:28:52]Swyx: Yeah. [00:28:52]Steve: The primary reason I can tell you this, because I was there, was that, was that, well, the steam-powered engine, no. The reason was that the deterministic stuff didn't scale. [00:29:06]Swyx: Yeah. Right. [00:29:06]Steve: They're using prologue, man, constraint systems and stuff like that. Well, that was a long time ago, right? Today, actually, these Chomsky-style systems do scale. And that's, in fact, exactly what Sourcegraph has built. Yeah. And so we have a very unique, I love the framing that Bjong's made, that the marriage of the Chomsky and the Norvig, you know, sort of models, you know, conceptual models, because we, you know, we have both of them and they're both really important. And in fact, there, there's this really interesting, like, kind of overlap between them, right? Where like the AI or our graph or our search engine could potentially provide the right context for any given query, which is, of course, why ranking is important. But what we've really signed ourselves up for is an extraordinary amount of testing. [00:29:45]Swyx: Yeah. [00:29:45]Steve: Because in SWIGs, you were saying that, you know, GPT-4 tends to the front of the context window and maybe other LLMs to the back and maybe, maybe the LLM in the middle. [00:29:53]Swyx: Yeah. [00:29:53]Steve: And so that means that, you know, if we're actually like, you know, verifying whether we, you know, some change we've made has improved things, we're going to have to test putting it at the beginning of the window and at the end of the window, you know, and maybe make the right decision based on the LLM that you've chosen. Which some of our competitors, that's a problem that they don't have, but we meet you, you know, where you are. Yeah. And we're, just to finish, we're writing tens of thousands. We're generating tests, you know, fill in the middle type tests and things. And then using our graph to basically sort of fine tune Cody's behavior there. [00:30:20]Swyx: Yeah. [00:30:21]Beyang: I also want to add, like, I have like an internal pet name for this, like kind of hybrid architecture that I'm trying to make catch on. Maybe I'll just say it here. Just saying it publicly kind of makes it more real. But like, I call the architecture that we've developed the Normsky architecture. [00:30:36]Swyx: Yeah. [00:30:36]Beyang: I mean, it's obviously a portmanteau of Norvig and Chomsky, but the acronym, it stands for non-agentic, rapid, multi-source code intelligence. So non-agentic because... Rolls right off the tongue. And Normsky. But it's non-agentic in the sense that like, we're not trying to like pitch you on kind of like agent hype, right? Like it's the things it does are really just developer tools developers have been using for decades now, like parsers and really good search indexes and things like that. Rapid because we place an emphasis on speed. We don't want to sit there waiting for kind of like multiple LLM requests to return to complete a simple user request. Multi-source because we're thinking broadly about what pieces of information and knowledge are useful context. So obviously starting with things that you can search in your code base, and then you add in the reference graph, which kind of like allows you to crawl outward from those initial results. But then even beyond that, you know, sources of information, like there's a lot of knowledge that's embedded in docs, in PRDs or product specs, in your production logging system, in your chat, in your Slack channel, right? Like there's so much context is embedded there. And when you're a human developer, and you're trying to like be productive in your code base, you're going to go to all these different systems to collect the context that you need to figure out what code you need to write. And I don't think the AI developer will be any different. It will need to pull context from all these different sources. So we're thinking broadly about how to integrate these into Codi. We hope through kind of like an open protocol that like others can extend and implement. And this is something else that should be accessible by December 14th in kind of like a preview stage. But that's really about like broadening this notion of the code graph beyond your Git repository to all the other sources where technical knowledge and valuable context can live. [00:32:21]Steve: Yeah, it becomes an artifact graph, right? It can link into your logs and your wikis and any data source, right? [00:32:27]Alessio: How do you guys think about the importance of, it's almost like data pre-processing in a way, which is bring it all together, tie it together, make it ready. Any thoughts on how to actually make that good? Some of the innovation you guys have made. [00:32:40]Steve: We talk a lot about the context fetching, right? I mean, there's a lot of ways you could answer this question. But, you know, we've spent a lot of time just in this podcast here talking about context fetching. But stuffing the context into the window is, you know, the bin packing problem, right? Because the window is not big enough, and you've got more context than you can fit. You've got a ranker maybe. But what is that context? Is it a function that was returned by an embedding or a graph call or something? Do you need the whole function? Or do you just need, you know, the top part of the function, this expression here, right? You know, so that art, the golf game of trying to, you know, get each piece of context down into its smallest state, possibly even summarized by another model, right, before it even goes to the LLM, becomes this is the game that we're in, yeah? And so, you know, recursive summarization and all the other techniques that you got to use to like stuff stuff into that context window become, you know, critically important. And you have to test them across every configuration of models that you could possibly need. [00:33:32]Beyang: I think data preprocessing is probably the like unsexy, way underappreciated secret to a lot of the cool stuff that people are shipping today. Whether you're doing like RAG or fine tuning or pre-training, like the preprocessing step matters so much because it's basically garbage in, garbage out, right? Like if you're feeding in garbage to the model, then it's going to output garbage. Concretely, you know, for code RAG, if you're not doing some sort of like preprocessing that takes advantage of a parser and is able to like extract the key components of a particular file of code, you know, separate the function signature from the body, from the doc string, what are you even doing? Like that's like table stakes. It opens up so much more possibilities with which you can kind of like tune your system to take advantage of the signals that come from those different parts of the code. Like we've had a tool, you know, since computers were invented that understands the structure of source code to a hundred percent precision. The compiler knows everything there is to know about the code in terms of like structure. Like why would you not want to use that in a system that's trying to generate code, answer questions about code? You shouldn't throw that out the window just because now we have really good, you know, data-driven models that can do other things. [00:34:44]Steve: Yeah. When I called it a data moat, you know, in my cheating post, a lot of people were confused, you know, because data moat sort of sounds like data lake because there's data and water and stuff. I don't know. And so they thought that we were sitting on this giant mountain of data that we had collected, but that's not what our data moat is. It's really a data pre-processing engine that can very quickly and scalably, like basically dissect your entire code base in a very small, fine-grained, you know, semantic unit and then serve it up. Yeah. And so it's really, it's not a data moat. It's a data pre-processing moat, I guess. [00:35:15]Beyang: Yeah. If anything, we're like hypersensitive to customer data privacy requirements. So it's not like we've taken a bunch of private data and like, you know, trained a generally available model. In fact, exactly the opposite. A lot of our customers are choosing Cody over Copilot and other competitors because we have an explicit guarantee that we don't do any of that. And that we've done that from day one. Yeah. I think that's a very real concern in today's day and age, because like if your proprietary IP finds its way into the training set of any model, it's very easy both to like extract that knowledge from the model and also use it to, you know, build systems that kind of work on top of the institutional knowledge that you've built up. [00:35:52]Alessio: About a year ago, I wrote a post on LLMs for developers. And one of the points I had was maybe the depth of like the DSL. I spent most of my career writing Ruby and I love Ruby. It's so nice to use, but you know, it's not as performant, but it's really easy to read, right? And then you look at other languages, maybe they're faster, but like they're more verbose, you know? And when you think about efficiency of the context window, that actually matters. [00:36:15]Swyx: Yeah. [00:36:15]Alessio: But I haven't really seen a DSL for models, you know? I haven't seen like code being optimized to like be easier to put in a model context. And it seems like your pre-processing is kind of doing that. Do you see in the future, like the way we think about the DSL and APIs and kind of like service interfaces be more focused on being context friendly, where it's like maybe it's harder to read for the human, but like the human is never going to write it anyway. We were talking on the Hacks podcast. There are like some data science things like spin up the spandex, like humans are never going to write again because the models can just do very easily. Yeah, curious to hear your thoughts. [00:36:51]Steve: Well, so DSLs, they involve, you know, writing a grammar and a parser and they're like little languages, right? We do them that way because, you know, we need them to compile and humans need to be able to read them and so on. The LLMs don't need that level of structure. You can throw any pile of crap at them, you know, more or less unstructured and they'll deal with it. So I think that's why a DSL hasn't emerged for sort of like communicating with the LLM or packaging up the context or anything. Maybe it will at some point, right? We've got, you know, tagging of context and things like that that are sort of peeking into DSL territory, right? But your point on do users, you know, do people have to learn DSLs like regular expressions or, you know, pick your favorite, right? XPath. I think you're absolutely right that the LLMs are really, really good at that. And I think you're going to see a lot less of people having to slave away learning these things. They just have to know the broad capabilities and the LLM will take care of the rest. [00:37:42]Swyx: Yeah, I'd agree with that. [00:37:43]Beyang: I think basically like the value profit of DSL is that it makes it easier to work with a lower level language, but at the expense of introducing an abstraction layer. And in many cases today, you know, without the benefit of AI cogeneration, like that totally worth it, right? With the benefit of AI cogeneration, I mean, I don't think all DSLs will go away. I think there's still, you know, places where that trade-off is going to be worthwhile. But it's kind of like how much of source code do you think is going to be generated through natural language prompting in the future? Because in a way, like any programming language is just a DSL on top of assembly, right? And so if people can do that, then yeah, like maybe for a large portion of the code [00:38:21]Swyx: that's written, [00:38:21]Beyang: people don't actually have to understand the DSL that is Ruby or Python or basically any other programming language that exists. [00:38:28]Steve: I mean, seriously, do you guys ever write SQL queries now without using a model of some sort? At least a draft. [00:38:34]Swyx: Yeah, right. [00:38:36]Steve: And so we have kind of like, you know, past that bridge, right? [00:38:39]Alessio: Yeah, I think like to me, the long-term thing is like, is there ever going to be, you don't actually see the code, you know? It's like, hey, the basic thing is like, hey, I need a function to some two numbers and that's it. I don't need you to generate the code. [00:38:53]Steve: And the following question, do you need the engineer or the paycheck? [00:38:56]Swyx: I mean, right? [00:38:58]Alessio: That's kind of the agent's discussion in a way where like you cannot automate the agents, but like slowly you're getting more of the atomic units of the work kind of like done. I kind of think of it as like, you know, [00:39:09]Beyang: do you need a punch card operator to answer that for you? And so like, I think we're still going to have people in the role of a software engineer, but the portion of time they spend on these kinds of like low-level, tedious tasks versus the higher level, more creative tasks is going to shift. [00:39:23]Steve: No, I haven't used punch cards. [00:39:25]Swyx: Yeah, I've been talking about like, so we kind of made this podcast about the sort of rise of the AI engineer. And like the first step is the AI enhanced engineer. That is that software developer that is no longer doing these routine, boilerplate-y type tasks, because they're just enhanced by tools like yours. So you mentioned OpenCodeGraph. I mean, that is a kind of DSL maybe, and because we're releasing this as you go GA, you hope for other people to take advantage of that? [00:39:52]Beyang: Oh yeah, I would say so OpenCodeGraph is not a DSL. It's more of a protocol. It's basically like, hey, if you want to make your system, whether it's, you know, chat or logging or whatever accessible to an AI developer tool like Cody, here's kind of like the schema by which you can provide that context and offer hints. So I would, you know, comparisons like LSP obviously did this for kind of like standard code intelligence. It's kind of like a lingua franca for providing fine references and codefinition. There's kind of like analogs to that. There might be also analogs to kind of the original OpenAI, kind of like plugins, API. There's all this like context out there that might be useful for an LM-based system to consume. And so at a high level, what we're trying to do is define a common language for context providers to provide context to other tools in the software development lifecycle. Yeah. Do you have any critiques of LSP, by the way, [00:40:42]Swyx: since like this is very much, very close to home? [00:40:45]Steve: One of the authors wrote a really good critique recently. Yeah. I don't think I saw that. Yeah, yeah. LSP could have been better. It just came out a couple of weeks ago. It was a good article. [00:40:54]Beyang: Yeah. I think LSP is great. Like for what it did for the developer ecosystem, it was absolutely fantastic. Like nowadays, like it's much easier now to get code navigation up and running in a bunch of editors by speaking this protocol. I think maybe the interesting question is like looking at the different design decisions comparing LSP basically with Kythe. Because Kythe has more of a... How would you describe it? [00:41:18]Steve: A storage format. [00:41:20]Beyang: I think the critique of LSP from a Kythe point of view would be like with LSP, you don't actually have an actual symbolic model of the code. It's not like LSP models like, hey, this function calls this other function. LSP is all like range-based. Like, hey, your cursor's at line 32, column 1. [00:41:35]Swyx: Yeah. [00:41:35]Beyang: And that's the thing you feed into the language server. And then it's like, okay, here's the range that you should jump to if you click on that range. So it kind of is intentionally ignorant of the fact that there's a thing called a reference underneath your cursor, and that's linked to a symbol definition. [00:41:49]Steve: Well, actually, that's the worst example you could have used. You're right. But that's the one thing that it actually did bake in is following references. [00:41:56]Swyx: Sure. [00:41:56]Steve: But it's sort of hardwired. [00:41:58]Swyx: Yeah. [00:41:58]Steve: Whereas Kythe attempts to model [00:42:00]Beyang: like all these things explicitly. [00:42:02]Swyx: And so... [00:42:02]Steve: Well, so LSP is a protocol, right? And so Google's internal protocol is gRPC-based. And it's a different approach than LSP. It's basically you make a heavy query to the back end, and you get a lot of data back, and then you render the whole page, you know? So we've looked at LSP, and we think that it's a little long in the tooth, right? I mean, it's a great protocol, lots and lots of support for it. But we need to push into the domain of exposing the intelligence through the protocol. Yeah. [00:42:29]Beyang: And so I would say we've developed a protocol of our own called Skip, which is at a very high level trying to take some of the good ideas from LSP and from Kythe and merge that into a system that in the near term is useful for Sourcegraph, but I think in the long term, we hope will be useful for the ecosystem. Okay, so here's what LSP did well. LSP, by virtue of being like intentionally dumb, dumb in air quotes, because I'm not like ragging on it, allowed language servers developers to kind of like bypass the hard problem of like modeling language semantics precisely. So like if all you want to do is jump to definition, you don't have to come up with like a universally unique naming scheme for each symbol, which is actually quite challenging because you have to think about like, okay, what's the top scope of this name? Is it the source code repository? Is it the package? Does it depend on like what package server you're fetching this from? Like whether it's the public one or the one inside your... Anyways, like naming is hard, right? And by just going from kind of like a location to location based approach, you basically just like throw that out the window. All I care about is jumping definition, just make that work. And you can make that work without having to deal with like all the complex global naming things. The limitation of that approach is that it's harder to build on top of that to build like a true knowledge graph. Like if you actually want a system that says like, okay, here's the web of functions and here's how they reference each other. And I want to incorporate that like semantic model of how the code operates or how the code relates to each other at like a static level. You can't do that with LSP because you have to deal with line ranges. And like concretely the pain point that we found in using LSP for source graph is like in order to do like a find references [00:44:04]Swyx: and then jump definitions, [00:44:04]Beyang: it's like a multi-hop process because like you have to jump to the range and then you have to find the symbol at that range. And it just adds a lot of latency and complexity of these operations where as a human, you're like, well, this thing clearly references this other thing. Why can't you just jump me to that? And I think that's the thing that Kaith does well. But then I think the issue that Kaith has had with adoption is because it is more sophisticated schema, I think. And so there's basically more things that you have to implement to get like a Kaith implementation up and running. I hope I'm not like, correct me if I'm wrong about any of this. [00:44:35]Steve: 100%, 100%. Kaith also has a problem, all these systems have the problem, even skip, or at least the way that we implemented the indexers, that they have to integrate with your build system in order to build that knowledge graph, right? Because you have to basically compile the code in a special mode to generate artifacts instead of binaries. And I would say, by the way, earlier I was saying that XREFs were in LSP, but it's actually, I was thinking of LSP plus LSIF. [00:44:58]Swyx: Yeah. That's another. [00:45:01]Steve: Which is actually bad. We can say that it's bad, right? [00:45:04]Steve: It's like skip or Kaith, it's supposed to be sort of a model serialization, you know, for the code graph, but it basically just does what LSP needs, the bare minimum. LSIF is basically if you took LSP [00:45:16]Beyang: and turned that into a serialization format. So like you build an index for language servers to kind of like quickly bootstrap from cold start. But it's a graph model [00:45:23]Steve: with all of the inconvenience of the API without an actual graph. And so, yeah. [00:45:29]Beyang: So like one of the things that we try to do with skip is try to capture the best of both worlds. So like make it easy to write an indexer, make the schema simple, but also model some of the more symbolic characteristics of the code that would allow us to essentially construct this knowledge graph that we can then make useful for both the human developer through SourceGraph and through the AI developer through Cody. [00:45:49]Steve: So anyway, just to finish off the graph comment, we've got a new graph, yeah, that's skip based. We call it BFG internally, right? It's a beautiful something graph. A big friendly graph. [00:46:00]Swyx: A big friendly graph. [00:46:01]Beyang: It's a blazing fast. [00:46:02]Steve: Blazing fast. [00:46:03]Swyx: Blazing fast graph. [00:46:04]Steve: And it is blazing fast, actually. It's really, really interesting. I should probably have to do a blog post about it to walk you through exactly how they're doing it. Oh, please. But it's a very AI-like iterative, you know, experimentation sort of approach. We're building a code graph based on all of our 10 years of knowledge about building code graphs, yeah? But we're building it quickly with zero configuration, and it doesn't have to integrate with your build. And through some magic tricks that we have. And so what just happens when you install the plugin, that it'll be there and indexing your code and providing that knowledge graph in the background without all that build system integration. This is a bit of secret sauce that we haven't really like advertised it very much lately. But I am super excited about it because what they do is they say, all right, you know, let's tackle function parameters today. Cody's not doing a very good job of completing function call arguments or function parameters in the definition, right? Yeah, we generate those thousands of tests, and then we can actually reuse those tests for the AI context as well. So fortunately, things are kind of converging on, we have, you know, half a dozen really, really good context sources, and we mix them all together. So anyway, BFG, you're going to hear more about it probably in the holidays? [00:47:12]Beyang: I think it'll be online for December 14th. We'll probably mention it. BFG is probably not the public name we're going to go with. I think we might call it like Graph Context or something like that. [00:47:20]Steve: We're officially calling it BFG. [00:47:22]Swyx: You heard it here first. [00:47:24]Beyang: BFG is just kind of like the working name. And so the impetus for BFG was like, if you look at like current AI inline code completion tools and the errors that they make, a lot of the errors that they make, even in kind of like the easy, like single line case, are essentially like type errors, right? Like you're trying to complete a function call and it suggests a variable that you defined earlier, but that variable is the wrong type. [00:47:47]Swyx: And that's the sort of thing [00:47:47]Beyang: where it's like a first year, like freshman CS student would not make that error, right? So like, why does the AI make that error? And the reason is, I mean, the AI is just suggesting things that are plausible without the context of the types or any other like broader files in the code. And so the kind of intuition here is like, why don't we just do the basic thing that like any baseline intelligent human developer would do, which is like click jump to definition, click some fine references and pull in that like Graph Context into the context window and then have it generate the completion. So like that's sort of like the MVP of what BFG was. And turns out that works really well. Like you can eliminate a lot of type errors that AI coding tools make just by pulling in that context. Yeah, but the graph is definitely [00:48:32]Steve: our Chomsky side. [00:48:33]Swyx: Yeah, exactly. [00:48:34]Beyang: So like this like Chomsky-Norvig thing, I think pops up in a bunch of differ
幻冬舎の暗号資産（仮想通貨）/ブロックチェーンなどweb3領域の専門メディア「あたらしい経済 https://www.neweconomy.jp/ 」がおくる、Podcast番組です。 ○解説したニュース ・米司法省とバイナンスが約6000億円で刑事調査解決の調整中か、CZの告発可能性も＝報道 ・Mint Townがアスターと提携、「キャプテン翼 -RIVALS-」等で「Astar zkEVM」導入検討 ・米SEC、未登録取引所運営及び資産管理混同でクラーケンを提訴、同社は反論も ・テザー社、人身売買シンジケート関連ウォレットの約332億円を自主凍結 ・ブロックワン子会社ブリッシュ、米コインデスクを100％買収 ・ODXのPTS取引市場「START」開始日が決定、第1号はいちごの不動産ST、Progmatで発行 ・Bittrex Global、12月14日で取引停止 ・「Phantom」ウォレットが「Cross-Chain Swapper」公開、アプリ内でトークンブリッジ可能に ・ソラナ上の分散型オラクルネットワーク「ピスネットワーク」、PYTHのエアドロップ開始 ○関連リンク ニュースの詳細や、アーカイブやその他の記事はこちらから www.neweconomy.jp/
あたらしい経済 「ポッドキャスト INTERVIEW」は、ブロックチェーンや暗号資産などWeb3領域のプレイヤーや有識者のインタビューをお届けするポッドキャスト番組です。今回は、アスターネットワーク（Astar Network）ファウンダーで、Startale Labs CEOの渡辺創太氏をゲスト出演いただきました。 渡辺氏に、先日発表したイーサリアム（Ethereum）のレイヤー2「Astar zkEVM Powered by Polygon」について、その新チェーンと既存のポルカドットチェーンとの連携、「Astar zkEVM」の今後のロードマップ、またソニーネットワークコミュニケーションズとStartale Labsで合弁会社を作り開発するブロックチェーンについて、語っていただきました。 （聞き手：あたらしい経済 設楽悠介） ●あたらしい経済はこちら https://www.neweconomy.jp/
The Daily Gwei Refuel gives you a recap every week day on everything that happened in the Ethereum and crypto ecosystems over the previous 24 hours - hosted by Anthony Sassano. Timestamps and links to topics discussed: https://daily-gwei-links.vercel.app/recent 00:00 Introductory song 01:08 Hashex joins ARK, Vaneck, 21Shares by filing spot Ethereum ETF https://twitter.com/JSeyff/status/1701612412456280467 04:40 SEC commissioners Peirce & Uyeda statement disagreeing with SEC's actions https://twitter.com/NFTherder/status/1702057933389836293 10:40 Lodestar incentive program results https://twitter.com/lodestar_eth/status/1701603041542365470 12:14 Holesky, Goerli's replacement testnet, live in next 30 hours https://twitter.com/beaconcha_in/status/1702212608881754184 14:28 Geth node fully synced on $129 Ethereym ARM board in 13 hours https://twitter.com/EthereumOnARM/status/1701891496645956028 17:11 Revoke Cash add Nefture feature to provide more insight into wallet health https://twitter.com/RevokeCash/status/1701935265886228781 18:56 OP Stack's Fault Proof System progress report https://twitter.com/OPLabsPBC/status/1702034469677326558 20:28 Polygon 2.0 kicks off with release of 3 improvement proposals https://twitter.com/0xPolygonLabs/status/1702280764677578755 21:37 Astar Network launch zkEVM powered by Polygon 21:50 Astar's expansion from Polkadot to Ethereum L2 https://twitter.com/TheBlock__/status/1701872109872447569 https://twitter.com/sandeepnailwal/status/1701990949319254345 22:47 Gnosis as an L2 https://twitter.com/gnosischain/status/1701929124070072498 25:07 Rollups and TV channels analogy https://twitter.com/sgoldfed/status/1701984082941341923 29:19 Metamask launch Snaps ecosystem for building features ontop of MM https://twitter.com/MetaMask/status/1701585470512095636 30:47 Account Abstraction using ZK Face ID https://x.com/knownothinglabs/status/1701895748508701013?s=20 This episode is also available on YouTube: https://youtu.be/SiWKn_WYxi4 Subscribe to the newsletter: https://thedailygwei.substack.com/ Subscribe on YouTube: https://www.youtube.com/channel/UCvCp6vKY5jDr87htKH6hgDA/ Follow Anthony on Twitter: https://twitter.com/sassal0x Follow The Daily Gwei on Twitter: https://twitter.com/thedailygwei Join the Discord Channel: https://discord.gg/4pfUJsENcg DISCLAIMER: All information presented across all of The Daily Gwei's communication channels is strictly for educational purposes and should not be taken as investment advice.
幻冬舎の暗号資産（仮想通貨）/ブロックチェーンなどweb3領域の専門メディア「あたらしい経済 https://www.neweconomy.jp/ 」がおくる、Podcast番組です。 ○解説したニュース ・アスターがイーサリアムL2参入、ポリゴンラボと協業で「Astar zkEVM」提供へ ・PayPal、暗号資産をドルに交換できる「オフランプサービス」提供開始 ・メタマスクがビットコインやソラナ等の非EVM対応可能に、コンセンシスが「MetaMask Snaps」オープンβ版公開 ・ヴィタリックがT-Mobileアカウント復活を報告、ハッキング騒動についても説明 ・「レイヤーゼロ」にグーグルクラウド・オラクル導入、チェーン間の全メッセージングが検証可能に ・英ゾディアカストディがシンガポールへ参入、機関投資家向けデジタル資産サービスを開始 ・英HSBCが米デジタル資産カストディFireblocksと提携か＝報道 ・ドリコムとチューリンガム開発の『Eternal Crypt - Wizardry BC -』、『STEPN』のFind Satoshi Labとweb3ファンドEmooteと提携
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
Today on the Ether we have Astar hosting NFT Month Twitter Space 2 exploring the Polkadot NFT ecosystem. You'll hear from The Kusamarian, Csaint02, Kusama Kingdom, Nao, riqi.near, and more! Recorded on May 16th 2023. Make sure to check out the two newest tracks from Finn and the RAC FM gang over at ImaginetheSmell.org! The majority of the music at the end of these spaces can be found streaming over on Spotify, and the rest of the streaming platforms. Check out Project Survival, Virus Diaries, and Plan B wherever you get your music. Thank you to everyone in the community who supports TerraSpaces.
Today on the Ether we have Galactica hosting Into the Cypher State discussing SEC regulation by enforcement. You'll hear from CosmosHOSS, Keystone, AStar.gala, Nourek.eth, ReubenMetcalfe, and more! Recorded on May 16th 2023. Make sure to check out the two newest tracks from Finn and the RAC FM gang over at ImaginetheSmell.org! The majority of the music at the end of these spaces can be found streaming over on Spotify, and the rest of the streaming platforms. Check out Project Survival, Virus Diaries, and Plan B wherever you get your music. Thank you to everyone in the community who supports TerraSpaces.
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
Gary chats to piper, pipe maker, teacher and composer, Fin Moore, about his music, craftmanship and his new project, an album featuring instruments all made by himself and his father, Hamish Moore, entitled The Piper and Maker 2: Celebrating C.Tracks Played Track 1 Breabach with The Ramparts (D MacLeod), We Were Poor But We Were Miserable (J D MacKenzie) from Astar https://www.breabach.com/ Track 2 Simon Fraser University Pipe Band with Alick Cameron, Champion Piper (GS MacLennan), Lexy MacAskill (J MacAskill), Lt Col DJS Murray (J Allan), Traditional, Thomson's Dirk, Back of the Moon (A G Kenneth) from Live From New York Cityhttps://sfupipeband.com/ Track 3 Seudan with The Rothiemurchas Rant, Alex Currie's, Lord MacDonald, Cota Mor Ealasaid, Hamish the Carpenter, The Margaree Reel (all trad, arr. Seudan, Grian Music) from Seudan, Greentrax Recordingshttps://www.greentrax.com/ Track 4 Fin Moore and Sarah Hoy with Jimmy mo Mhile Stoer, Gillean nan Drobher, Dinkie Dorrian's (Francie Dearg O'Biern) from The Piper and the Maker 2 – Celebrating Chttps://moorepipes.bandcamp.com/album/piper-and-the-maker-ii-celebrating-c Track 5 Allan MacDonald with Siud mar Chaidh an Cal a Dholaidh (trad) from The Piper and the Maker 2 – Celebrating Chttps://moorepipes.bandcamp.com/album/piper-and-the-maker-ii-celebrating-c Track 6 Seonaidh MacIntyre and Ewen Henderson with Finn's Tune, Bàgh Dubh gu Buala Dubh, and Allan J. Nairn of Ceann Tràgha (all Seonaidh MacIntyre) from The Piper and the Maker 2 – Celebrating Chttps://moorepipes.bandcamp.com/album/piper-and-the-maker-ii-celebrating-c Track 7 Mairi Campbell and Hamish Moore, The Piper and the Maker (M Campbell and D Francis), from The Piper and the Maker (Greentrax Recordings)https://www.greentrax.com/ Links Mentionedhttps://www.pipesdrums.com/https://bagpipe.news/https://www.thebigrabshow.com/Get in TouchEYP@garywest.scothttps://www.garywest.scot/ Support the show
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
Tune in for the return of MOLIAE Short Stories with host, Nichel Anderson, that will be featuring of the whereabouts of General Hanee of the Orion Regime as he discovers a familiar someone close to his family that will set off to another position of reasons, after Hanee grand escape from the Orions Powers of Leadership and of the Unkeno Council members that were of opposition to his position of reasoning in the latest attempt for the Astar of Mitsrayim. -- SEASON 6 Show Episodes Releases on Monday by 10am EST October 17, 2022 - "Why I emphasis love in my short stories" November 14, 2022 - "Part 8: Atlantis: We are amongst the truth seekers of Akuni" December 12, 2022 - "Episode 63 'Queen Hagar enters the Reality of Orion" January 16, 2023 - "Character Breakdown of Atlantis" February 20-24, 2023 - "Power of Reading Week Long Event" releases episodes Mon, Wed, and Fri March 13, 2023 - Director Clip Notes Mahogany Part 4, published 1/14/2019" April 10, 2023 - "MOLIAE Short Stories Epi #64 Hanee sends a letter to King Mahlon for an Allegiance" May 22, 2023 - FINALE SHOW Season 6 MOLIAE Short Stories Epi #65 King Mahlon speaks of disloyalty before the GODS of Pleiadeans and an Orion is born" - Summer Break 2023 June 26, 2023 July 31, 2023 August 28, 2023 -- FOLLOW MOLIAE on YOUTUBE - livestreaming & giveaways YouTube Channel - subscribe today https://www.youtube.com/moliae --- ANNOUNCEMENTS NFTs Collection "Pyramid Mystery Temple Reunion" PMTR the MINT date is TBA. Visit official website: MOLIAEWorld.com MOLIAE Token is TBA get ready plans to be utilize on official cryptocurrency, NFTs website MOLIAEWorld.com Follow this MOLIAE Project on twitter.com/MOLIAEWorld & share it. PMTR NFTs collection is of 10,000 classmates that are pixel pyramids on Ethereum blockchain with symbolism and meaning with numerology and astrology as our ancient ancestors did so in Sumner and Ancient Egypt/Mitsrayim. In this collection theme purpose, additionally, the mystery to whom took their swords is uptmost concern, in particular, the 24 HUJTA swords (there are more swords assigned to each pyramid). --- MOLIAE Music "When Love Was Divine" now available for download as the anticipated wait for the album. -- MOLIAE Comic book series episodes will be announce for the timeline before the podcast series - "The disloyalty amongst the Tribal Leaders" available on Webtoons to catch up on this saga series: Vol 1 "A Deal Was Made In The Cosmos" -- FOLLOW MOLIAE on Social Media & Share this! Instagram Pages - Follow us, Share this: MOLIAE8 : https://www.instagram.com/moliae8 and… MOLIAEBeauty8 : https://www.instagram.com/moliaebeauty8 — Facebook Production of MOLIAE https://www.facebook.com/moliae SkinCare Beauty brand for Kings and Queens https://www.faebook.com/moliaebeauty Twitter Social Page https://www.twitter.com/moliae Skincare Twitter Page: https://www.twitter.com/moliaeb -- Tune and follow, share it with someone else and subscribe to MOLIAE enewsletter at moliae.com Buy The Book: "Mitsrayim: A Memoir of A Past Life In Ancient Egypt" Available on Amazon.com -- Support This Podcast Show - Buy Our T-shirts https://moliae.com/collections/moliae-tshirts-and-hoodies ---- Check Out Brand of Essential Body Oils At: MOLIAE Beauty Shop: Https://moliaebeauty.com Get Our Signature Body Oil “Ankh Ra 360”: https://moliaebeauty.com/products/moliae-ankh-ra-360-body-oil GIFT BOX KITS ! You want to send Ancient Egypt in a beautiful one of a kind present treasure chest? Order our gift box kits and be like a Royal ! You remember the times.. https://moliaebeauty.com/collections/gift-box-kits
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
Finally a new generation has arrived, this time with the TRS-80! Color finally! You love to see it. We cover Football on the new computer, as well as Crystalware's Beneath the Pyramids, The Bomber from Astar, and Ricochet from Softside.Website -https://historyofvideogamespodcast.comTwitter - https://twitter.com/HistoryofVideo1Email - historyvgpodcast@gmail.comHosts - Ben & WesMusic - Arranged and recorded by BenCan you guess this week's transition music? The theme is 'Best of Ben'
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
Bob and Brad continue their miniseries of Michael Mann films with his 1995 crime epic Heat, a movie that Bob had surprisingly never seen until about a year ago. This is Brad's first time watching the much-hyped pairing of Robert De Niro and Al Pacino, and the guys have plenty of thoughts on the two lead performances. Mixed in is a review of Val Kilmer, as well as a serial-killer subplot that's entirely unnecessary to the film. Meanwhile, they return to one of their favorite scotch whisky brands to try Glenmorangie Astar. This special-release scotch aged in ex-bourbon casks is still available on the market, albeit at around a $100 price tag. One of our hosts loves this whisky, while the other is in a decidedly different spot. Film & Whiskey Podcast. New episodes every Monday. Film & Whiskey Instagram Film & Whiskey Facebook Film & Whiskey Twitter Email us! Join our Discord server! Theme music: "New Shoes" by Blue Wednesday --- Send in a voice message: https://anchor.fm/filmwhiskey/message Support this podcast: https://anchor.fm/filmwhiskey/support
"Introduction to Islamic Metaphysics" by Shaykh Mohamed Faouzi al-Karkari (qs) — Translation & Voice by Yousef Casewit & Khalid Williams. Buy the Book: https://amzn.to/3eeeyZh
Lukas Nelson stops by the podcast for the first time to talk about working on the soundtrack for and playing in 'A Star is Born' alongside Bradley Cooper and Lady Gaga, some of his favorite artists in country right now, how he chipped his tooth playing guitar and why he never got it fixed, how his dad, the legendary Willie Nelson, is his favorite songwriter and his mission to carry on that legacy through his own music.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
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