Podcasts about Versa

Evalúan solicitudes de instituciones que buscan convertirse en bancosRamón de la Fuente representará a México en la CELACAutoridades de la CDMX y Edomex ejecutan Operación FortalezaMás información en nuestro Podcast

Welcome back to the second episode of the pod! This week we will be discussing our experience of the visa process and finding accommodation on our year abroad. As always, if there are any burning questions you would like us to discuss, feel free to send them here: wrf102@student.bham.ac.uk.here's Hajera's article on chatting with our ex-prime minister: https://theuoblinguist.com/2024/07/16/british-polyglots-an-endangered-species/

durée : 00:59:55 - Gerald Clayton "Ones & Twos" - par : Nicolas Pommaret - Le nouvel album du pianiste et compositeur Gerald Clayton est une collection de musique originale habilement écrite, pleine de groove et tournée vers l'avenir, interprétée par quelques-uns des meilleurs musiciens improvisateurs de leur génération. “Ones & Twos” paraît chez Blue Note.

We have Team Rutus with us on the show discussing the Rutus Versa metal detector which is a hidden gem machine. Plus win prizes in our FREE PRIZE DRAW. YOU MUST BE WATCHING THE SHOW TO CLAIM YOUR PRIZE. UK VIEWERS ONLY.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-big-detecting-show--3690873/support.

Happy New Year to all our viewers. Tonight we have TC Detects announcing the competition to win a Rutus Versa Kindly supplied by Detecnicks Ltd and RUTUS Metal Detectors.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-big-detecting-show--3690873/support.

What does the next season of leadership look like for the church? Where are we going and how do we be the most emotional stable leaders possible.

Pace fatta tra il fisco italiano e Google dopo che l'azienda californiana ha versato 326 milioni di euro. E la Procura di Milano, che indagava per evasione fiscale sulla Google Ireland Limited, chiede l'archiviazione del procedimento.

Stāsta horeogrāfe, dejotāja, baleta pedagoģe, Latvijas Kultūras akadēmijas profesore Gunta Bāliņa. No 38 Viljama Šekspīra (1564–1616) lugām dažādos laika posmos ir iestudēti vairāk nekā 20 baleti un citu dejas žanru skatuves darbi: “Antonijs un Kleopatra”, “Hamlets”, “Makbets”, “Romeo un Džuljeta”, “Otello”, “Sapnis vasaras naktī”, “Spītnieces savaldīšana” un citi. Sākotnēji arī paša Šekspīra lugās tikušas izmantotas dažādas tā laika galma dejas: burē, čakona, gavote un citas. Kopš 18. gadsimta horeogrāfi jau veidoja baletus, iedvesmojoties  no Šekspīra lugām. Vairāki avoti apstiprina, ka šī ideja pieder franču horeogrāfam un dejas teorētiķim Žanam Žakam Novēram (Jean-Georges Noverre). 1761. gadā viņš  Francijas galmā iestudēja baletu “Antonijs un Kleopatra”. Mūzikas autors bija Versaļā dzimušais vācu izcelsmes komponists Rūdolfs Kreicers (Rodolphe Kreutzer). Savukārt itāļu komponistu un horeogrāfu Frančesko Kleriko (Clerico) bija iedvesmojusi traģēdija “Hamlets”. Līdz pat mūsu dienām šis caururbjošais atriebības stāsts (Hamlets) ir ieintriģējis un izaicinājis māksliniekus, tostarp horeogrāfus. Savas versijas īstenojuši tādi pasaulslaveni horeogrāfi kā Džons Noimeiers (John Noimayer), Kenets Makmilans (Keneth McMillan), Deivids Niksons (David Nixon), Radu Poklitaru un citi. Šekspīra lugai “Otello, Venēcijas moris” pievērsies viens no modernās dejas ietekmīgākajiem pamatlicējiem, meksikāņu horeogrāfs Hosē Limons (José Arcadio Limón). Viņš radīja 20 minūšu garu izrādi „Mora pavana” ar Henrija Pērsela mūziku. Pēc paša Hosē Limona, kurš dejoja oriģinālversijā, viens no nozīmīgākajiem Otello lomas interpretiem bijis Rūdolfs Nurijevs. Neapšaubāmi viens no populārākajiem sava žanra paraugiem ir balets „Romeo un Džuljeta” pēc Šekspīra ģeniālās traģēdijas motīviem, kas daudzkārt izmantoti arī  citos mākslas žanros. Pats pirmais šī stāsta horeogrāfiskās partitūras izveidotājs bija itālis Eusebio Luci (Eusebio Luzzi), kurš 1785. gadā Venēcijā  iestudēja baletu ar Luidži Mareskalki (Luigi Marescalchi) mūziku. Turpmāko versiju vidū jāmin 19. gadsimta sākumā tolaik slavenā horeogrāfa  Vinčenco Galeoti (Vincenzo Galeotti) iestudējumu Dānijas Karaliskajā teātrī. Tam mūziku radīja Klauss Nīlsens Šalls (Claus Nielsen Schall), kurš darbojās gan kā vijolnieks, gan dejotājs un baletmūzikas autors. Savu “Romeo un Džuljetas” versiju 1833. gadā Kopenhāgenā uzveda Galeoti audzēknis Augusts Burnonvils (August Bournonville). Starp citu Burnonvila slavenāko baletu vidū ir arī mūsu baleta repertuārā izrāde “Silfīdas”. Intriģējoši pavērsieni saistīti ar Sergeja Prokofjeva 1934. gada ieceri. Sākotnējais “Romeo un Džuljetas” librets paredzēja mainīt stāsta beigas, ļaujot jaunajiem mīlētājiem laimīgi turpināt dzīvi. Taču padomju laika represiju gaisotnē tomēr tika nolemts pieturēties pie traģiskā noslēguma. Kad Lielā teātra direkcija pasludināja šo mūziku par nedejisku un nepiemērotu, komponists izveidoja vairākas simfoniskās svītas, kas turpina savu patstāvīgo skatuves dzīvi līdz pat mūsdienām. Prokofjeva baleta pirmizrāde, gan nepilnā versijā, notika 1938. gadā Brno. Horeogrāfs bija tā laika starptautiski atzītā čehu slavenība, Ivo Psota. Krievijas pirmizrāde jau pilnā apjomā Leonīda Lavrovska iestudējumā notika Sanktpēterburgā 1940. gadā. Tā filmas versija (1955) tika godalgota ar Kannu kinofestivāla Zelta palmas zaru. Gan Eiropā, gan aiz okeāna vēl aizvien tiek uzvesta Prokofjeva mūzikā balstītā horeogrāfa Džona Kranko “Romeo un Džuljeta”. Vēl aizvien Anglijas Nacionālā baleta repertuārā ir Rūdolfa Nurijeva 1977. gada versija, izveidota par godu Karalienes Elizabetes Otrās valdīšanas Sudraba jubilejai. Bet baleta sākotnējo, laimīgo beigu versiju, 2008. gadā, balstoties uz  Prinstonas Universitātes profesora Saimona Morisona (Simon Morrison) pētījumiem, iestudēja amerikāņu horeogrāfs Marks Moriss (Mark William Morris). Viljama Šekspīra literāro darbu dramaturģija, tēlu daudzveidība, to psiholoģiskā sarežģītība un daudzpusība, kā arī filozofiskais dziļums turpina piesaistīt komponistus un horeogrāfus arī mūsdienās. Latvijas Nacionālajā Operas un  baleta teātrī  iestudēti seši Šekspīra literārajos  darbos balstītie  baleti: Aleksandra Lemberga “Antonijs un Kleopatra” (1972), Lāslo Šeregi “Spītnieces savaldīšana” (2001), Jurija Vamoša “Sapnis vasaras naktī”(2010), Allas Sigalovas “Otello” (2012), Antona Freimana “Hamlets”(2019), bet “Romeo un Džuljeta” pat vairākkārt: Jevgēņija Čangas inscenējumā ar Annu Priedi un Haraldu Ritenbergu, kā arī Aleksandra Lemberga, Vladimira Vasiļjeva un Valentīnas Turku oriģinālajās versijās.   Baleta viencēliens “Hamlets” ar Rahmaņinova un Lindas Leimanes mūziku aicina skatītājus arī šodien.

Mākslas muzejā "Rīgas Birža" 24. janvārī tika atklāta pasaulslavenā franču mākslinieka, konceptuālās mākslas pioniera Bernāra Venē darbu retrospekcija "Bernārs Venē. Glezniecība: no racionālā uz virtuālo. 1966-2024". Kā lai raksturo Bernāru Venē? Varētu sacīt, cilvēks – orķestris. Mākslinieks, kurš radoši izpaudies visdažādākajos medijos: glezniecība, zīmējums, tēlniecība, instalācija, skatuves dizains, horeogrāfija, kino, dzeja un arī mūzika. Daudziem Bernāra Venē vārds pirmkārt saistās ar vērienīgām monumentālām formām, ar Beļģijā atklāto vides objektu – 250 tonnas smago un 60 metrus augsto "Arc Majeur", kas joprojām ir augstākais vides mākslas darbs publiskajā telpā. Bernāra Venē veidotais 18 metrus augstais tērauda objekts – veltījums olimpiskajai lāpai – ir vienīgais publiskais mākslas darbs, kas tika radīts speciāli 2024. gada vasaras Olimpiskajām spēlēm Parīzē. Venē ir bijis viens no nedaudzajiem mākslas milžiem, kurš uzaicināts rīkot izstādi Versaļas pilī. Viņa  darbi skatāmi vairāk nekā 70 pasaules muzejos, tai skaitā Modernās mākslas un Gugenheima muzejos Ņujorkā, Hiršhorna muzejā un skulptūru dārzā Vašingtonā, Pompidū centrā Parīzē un vēl daudzviet citur, kā arī pasaules vadošajās izstāžu zālēs, galerijās un laikmetīgās mākslas kolekcijās. Izstādi Mākslas muzejā “Rīgas Birža” producē kultūras portāls Arterritory.com sadarbībā ar Bernāra Venē studiju un Latvijas Nacionālo mākslas muzeju. Kuratores ir Una Meistere un Daiga Rudzāte, bet projekta vadītāja – Vita Birzaka. Dienu pirms izstādes atklāšanas Mākslas muzejā "Rīgas Birža" Inta Zēgnere tikās ar Bernāru Venē. Mums ir liels gods satikt jūs šeit Rīgā un iepazīt jūsu izstādi. Kādas ir jūsu attiecības ar Rīgu? Biju Rīgā pirms kādiem pieciem, sešiem gadiem, kad mana sieva Diāna bija šurp atvedusi mākslinieku rotu kolekcijas izstādi, kurā bija skatāmi Pikaso, Maksa Ernsta, Hansa Arpa, Džakometi, Fontanas, Džefa Kūnsa un vēl citu autoru darbi. Tā ir kolekcija, kas ceļo pa visu pasauli un tagad aprīlī būs skatāma Floridā, Nortona muzejā. Bet tad, kad bija izstāde Rīgā, arī es, protams, ierados uz atklāšanu. Tā pirmo reizi arī ieraudzīju Rīgu. Un tobrīd izlēmāt, ka vēlētos šeit parādīt savus darbus? Ne gluži tā. Una Meistere, kas ir brīnišķīgs cilvēks un ar kuru mums vienmēr ir ļoti jaukas attiecības, reiz pēkšņi vaicāja: "Bernār, kā būtu, ja kādreiz Rīgā muzejā tu izveidotu izstādi? Teicu, ka labprāt to vēlētos, jo man patīk komunicēt. Man tas ir ļoti svarīgi. Man patiešām daudz svarīgāk ir runāt par mākslu nekā pārdot mākslas darbus un tikai pelnīt naudu. Protams, tā palīdz jums turpināt un darīt lielas lietas Taču patiesībā tieši komunikācija ir mākslas būtība, lai mainītu cilvēku  uztveres jūtīgumu, palīdzētu  viņiem saprast, ka ir arī citi veidi, kā domāt par mākslu. Tā šī iespēja rīkot izstādi Rīgā radās, un esmu ļoti priecīgs, ka varu savus darbus šeit parādīt. Uzreiz no lidostas devāties uz muzeju, lai redzētu, kā izskatās ekspozīcija. Vai esat apmierināts ar to, kā jūsu darbi "jūtas" šeit, šajā muzejā, šajā zālē, kā tie veido dialogu ar telpu? Vispār ir ļoti grūti eksponēt laikmetīgo mākslu šādā vidē, kas ir tik piesātināta, tik pašpietiekama. It īpaši, ja tie ir ļoti vienkārši, konceptuāli darbi. Bet tas, kā tas ir paveikts, patiešām ir izdevies ļoti labi. Par laimi manas gleznas ir lielas un uzreiz "piesaka sevi", piesaista uzmanību. Ja tie būtu mazi darbi, tad apkārtējais būtu nedaudz traucējošs. Taču šajā gadījumā esmu pilnībā apmierināts, arī ar darbu izlasi. Man šobrīd notiek vērienīga izstāde Ķīnā, kur skatāmi 180 darbi, un šovasar būs izstāde Francijas dienvidos, Pikaso muzejā Antibā. Izstāde, kas skatāma šobrīd Rīgā, pēc tam ceļos uz turieni. Mēs runājām par dialogu starp telpu un jūsu darbiem. Vide, kur skatāmi jūsu darbi, ir ļoti dažāda. Atceros fantastisko izstādi Versaļā, pie Versaļas pils  vai, piemēram, vides objektus Vandomas laukumā Parīzē. Savukārt pagājušajā gadā Venēcijas biennāles laikā Venēcijas Nacionālajā bibliotēkā bija skatāmi jūsu agrīnie darbi, tai skaitā arī slavenā instalācija "Ogļu kaudze" un gleznojumi ar darvu. Esmu paveicis tik daudz pēdējo 65 gadu laikā. Jā, izstāde Ķīnā ir veltīta maniem 65 darba gadiem, un tur manā rīcībā ir 10 000 kvadrātmetru, lai darbus parādītu. Bet patiesībā tas, ko minējāt, runājot par Venēciju, ir ļoti labs piemērs, kā darbiem izdzīvot pašpietiekamā vidē. Venēcijas Nacionālā bibliotēka nav baltais kubs, tur ir krietni jāpiestrādā, sadarbojoties ar vidi, un bija interesanti "sacensties" vienā telpā ar Ticiānu, Tintoreto un Veronēzi - šiem lielajiem puišiem. Tas bija diezgan biedējoši, bet izdevās. Tas labi izdevās. Bija  vienkārši jāveido sienas un jāciena citu mākslinieku darbs. Lai tavs darbs nepazustu, tam ir jābūt pietiekami spēcīgam un iespaidīgam. Jūs pieminējāt Versaļu. Versaļa arī bija liels izaicinājums, jo ļoti maz mākslinieku spēj eksponēt savus darbus pie Versaļas pils. Tāpēc nolēmu izveidot milzīgas skulptūras no arkām, kas ir apmēram 25 metrus augstas, un tas izdevās labi. Tas vienmēr ir izaicinājums, ja jums ir darīšana ar ainavu vai arī ar greznu telpu muzejā. Bet atgriezīsimies pie izstādes muzejā "Rīgas birža" un jūsu retrospekcijas, kas aptver laikaposmu no 1966. līdz 2024. gadam. Tā ļauj palūkoties uz visu padarīto it kā no attāluma, ieraudzīt paveikto. Vai jums pašam ir interesanti paskatīties atpakaļ uz pirmsākumiem? Lielāko daļu sava laika pavadu, skatoties uz priekšu, jo ir tik daudz  jārada. Es labi apzinos, ko esmu paveicis. Kas padarīts, tas padarīts, bet man jāsteidzas vēl tik daudz ko paveikt. Tajā pašā laikā, jā, ir interesanti parādīt, kā šis darbs ir attīstījies. Daudziem to skatīt ir ļoti mulsinoši, joo viņi pazīst mani kā tēlnieku, tad viņi redz gleznas, tad cita veida gleznas, piemēram, tās, kas gleznotas ar darvu. Tad viņi ierauga šo reljefos darbus uz kartona... Viņi pastāvīgi redz citas lietas, un tas mulsina. Retrospekcijas ir ļoti vērtīgas, jo varat parādīt darba evolūciju, bet pati koncepcija man gandrīz nav mainījusies. Ziniet, mana māksla ir tā, ko mēs saucam par pašreferenci, kas nozīmē, ka darbs runā tikai par sevi, ka tam nav atsauces uz apkārtējo pasauli. Nav atsauces uz maniem filosofiskajiem vai reliģiskajiem uzskatiem. Nē, es runāju par mākslu, konkrēti par mākslu. Tas ir tāpat kā matemātiķim. Kad viņš nodarbojas ar matemātiku, viņš nerunā par kaut ko citu. Viņš runā par matemātiku un veic pētījumus  tieši šajā jomā. Kad es radu mākslu, tas ir tas pats. Izstādē mēs redzam manus matemātiskos pirmsākumus ar šo tik ļoti svarīgu diagrammu no 1966. gada. Tas bija laiks, kad pārtraucu nodarboties ar mākslu, bet pēc tam atkal atsāku ar jaunām idejām, jaunu potenciālu, interesantām lietām. Varam redzēt šo radošo evolūciju, kas joprojām turpinās. Bet kas ir interesanti, un es ļoti vēlos to akcentēt - tās ir pēdējās četras gleznas, kas skatāmas izstādē, un kas radītas, izmantojot ģeneratīvo mākslu. Tas ir pārsteidzoši, ka mākslinieks var dot norādījumus datoram, un dators, šī mašīna, var radīt mākslas darbu. Šo darbu pamatā ir sarežģīta datorprogrammēšana, algoritmu ģenerētu tēlu digitālas gleznas. Bet ziniet, es gāju vēl tālāk par to. Es jums pastāstīšu, ko darīju, sadarbojotoies ar  Perrotin galeriju Parīzē. Veidojot šos darbus, devu norādes datorspeciālistam-programmētājam, lai tiktu radīti dažādi leņķi, norādīju, kādām ir jābūt proporcijām un lielumam. Tad viņš vaicāja - kāda krāsā? Atbildēju - visas  krāsas ir iespējamas. Labi, cik daudz jūs vēlaties? Un es teicu, ka no 20 līdz 60. Tātad mašīna pati vēlāk izlems. Un kāda būs to kustība plaknē? Ak, tai ir jākrīt no ekrāna augšas uzleju un jānokrīt tādā konfigurācijā, kā dators to izlems. Tātad devu šos norādījumus, un, kā redzat, es nepateicu, kā visam jāizskatās un kādu krāsu izvēlēties. Tad programmētājs nospieda pogu un iznāca desmit gatavi attēli, kurus es pat neapskatīju. Tie tika nosūtīti uz Francijas dienvidiem, kur tika nokopēti uz kanvas. Tā bija mašīna, kas to paveica, izmantojot konkrētas krāsas tinti un tā tālāk. Tad darbi tika ierāmēti, nosūtīti Perrotin galerijai Parīzē. Notika izstādes atklāšana, es negāju uz to. Pēc tam gleznas tika pārdotas. Es tās neredzēju un nekad arī neredzēšu. Tāds ir mans sapnis, mans mērķis. Varbūt kādreiz es kādu no tām arī ieraudzīšu, ja kāds no kolekcionāriem, kas nopirka, mani uzaicinās vakariņās un tur es to pēkšņi ieraudzīšu un varēšu teikt: "Ak, cik interesanti. Mana glezna, ko es nekad dzīvē neesmu redzējis". Un tas, ko saku, ir ļoti nopietni. Runa ir par idejām, par to, ka vēlamies parādīt, kāda var būt māksla. Mēs zinām, kas līdz šim ir bijusi māksla, taču robežas ir bezgalīgas. Un šeit es to parādu, vadoties no oriģinālās koncepcijas, jo es esmu tās radītājs. Šo darbu neesmu gleznojis ar roku, bet tas tiešām ir mans darbs, jo tā ir mana koncepcija. Ziniet, Rafaelam savulaik, kad viņš gleznoja Vatikānā, bija 300 palīgu. Viņam bija daži, kas gleznoja debesis, daži kolonnas, un tā tālāk. Tātad, tas nebija tikai Rafaela rokas pieskāriens. Mikelandželo ir teicis: "Non fa l'arte col la mano, ma col cervello". Mēs neradām mākslu ar roku, mēs to radām ar smadzenēm". Vēlos paplašināt šīs uztveres galējās robežas. Man ir koncepcija, es to attīstu un turpinu šo izpēti. Es neteikšu -  ak, neviens to nesapratīs, ak, kā tas izskatīsies? Nē. Man tas ir jādara, un es to daru. Un, lūk, šī ir otrā reize, kad izstādu šos darbus un divus no tiem, kas tagad ir skatāmi Rīgā, pats līdz šim nebiju redzējis. Es tos redzēju vienīgi ekrānā, bet nebiju redzējis tos pabeigtus, jo mans palīgs to darīja, kamēr biju Ķīnā. Pēdējie darbi ir loģisks turpinājums tam, ko esat darījis jau agrāk, tikai tagad izmantojot citus rīkus, piemēram, datorprogrammu? Tieši tā. Gleznās no 60. gadiem jūs redzat šo matemātisko pieeju. Es izvēlējos matemātiskas formulas no zinātniskām grāmatām to oriģinalitātes dēļ, jo šie darbi nemaz neizskatās pēc mākslas. Ja es nodarbojos ar mākslu, kas izskatās pēc mākslas, tā nemaz nav māksla, tā ir tikai apmierinātība ar sevi, kopējot to, kas paveikts, un nav nekādas piepūles manām smadzenēm. Īsti mākslinieki kā Sezāns, Pikaso, Matiss - viņiem nodarbošanās ar mākslu bija ciešanas, jo, to darot, viņi gribēja izmainīt mākslas vēsturi. Arī es esmu kā matemātiķis, kurš diendienā strādā pie vienādojumiem, mēģinot pārvarēt sevi un pārsniegt robežas tam, kā cilvēki ir domājuši pirms jums. Tad, kad mākslinieks nāk ar jaunu koncepciju, ar kaut ko pilnīgi oriģinālu, viņš nāk ar ko tādu, par ko cilvēce pirms viņa nekad nav domājusi? Ziniet, ir viegli darīt lietas, kā tas ir darīts tradicionāli 2000 gadus, bet, ja runājam par radīšanu, mēs runājam par koncepta maiņu, tāpēc tas tiešām ir ļoti, ļoti nopietni. Ja matemātiskās gleznas tika ņemtas  no grāmatām, tad izmantojot algoritmus es dodu norādījumus, un saņemu to, ko jūs redzat. Cilvēki vienmēr runā par interpretāciju. Jūs skatāties uz mākslas darbiem un  sakāt - ko tad tas nozīmē? Kāds ir stāsts? Gleznai vai skulptūrai ir stāsts, tā runā nevis pati par sevi, bet par kaut ko citu. Taču kopš  1910., 1912. gada tādi cilvēki kā Kandinskis, Delonē, Pikabija un citi sāka ieviest abstrakciju. Visbeidzot, pateicoties abstraktajai mākslai, mūsu priekšā bija glezna, kas attēloja sevi, un tikai sevi nevis pasauli ārpus tās. Arī es esmu uz šī pašreferences ceļa. Tāpēc, kad eksponēju leņķi, cilvēki varētu domāt, ka tās, piemēram, ir kā šķēres, jo cilvēki vienmēr vēlas atrast kādu analoģiju, meklē asociācijas, nevis saka - jā, tas ir leņķis. Viņi meklē ko citu. Tāpēc es precīzi norādu, cik grādu ir šim leņķim, lai viņi zinātu, ka tas ir 40 grādu leņķis un nekas cits. Plašāk - audioierakstā.

#704 Show Notes:  https://wetflyswing.com/704  Presented by: Pescador on the Fly Sponsors: https://wetflyswing.com/pescador  In this episode, we sit down with Jeff Ditsworth, owner of Pescador on the Fly, to talk about the perfect packable fly rod: their six-piece travel rod. Jeff, an expert in fly fishing travel, shares insights into his innovative line of rods, including the El Jefe and El Rey, which are designed to make traveling with your fishing gear easier and more efficient. We delve into the common misconceptions about multi-piece rods and explore the exceptional quality and convenience they offer. Plus, Jeff reveals the inspiration behind the creation of the Trout Hero bag for Trout Unlimited and shares his daily routine that keeps him at the top of his game. Join us as we uncover how this game-changing equipment could transform your fly fishing trips and simplify your travels. Episode Chapters with Jeff Ditsworth on The Perfect Packable Fly Rod 1:55 - Jeff recalls his first introduction to fly fishing. He used to go camping with his father and grandfather, both of whom owned very old fly rods. His first fishing experiences were on lakes and ponds, targeting species like bluegills and bass. 3:44 - He shares his journey of creating the brand Pescador on the Fly. While traveling for work, he found it cumbersome to carry traditional four-section rods and sought a better travel rod. He spent two years in research and development, eventually launching the brand with the El Cinco, a seven-section rod. 9:54 - We dig into their six-section El Rey rod. Jeff mentions that the rod was recently featured in Fly Fisherman magazine's 2025 gear guide, highlighting its recognition in the industry. 12:35 - We ask him for tips on going on a travel for fishing trips. Jeff emphasizes the importance of traveling with more than one rod as a backup. He mentions the unpredictability of trips, such as rods breaking due to accidents, and suggests bringing a lightweight, compact rod like the six-section rod that fits easily into a backpack. Jeff also suggests varying the types of rods based on the fishing environment, such as bringing different weights for different water sizes and fish types. 14:48 - We get into their El Jefe line that includes rods from zero to 10-weight, available in both four and six-piece configurations. He mentions the affordability of their fishing combos making them accessible to newcomers to fly fishing. 18:42 - Jeff criticizes the industry's tendency to overcomplicate fly fishing with too many options, which can intimidate newcomers. He suggests that simplifying the approach to gear is often sufficient for most anglers. 23:22 - Jeff discusses the origins of the names for the brand and its product lines. He explains that "Pescador" means fisherman, a name inspired by his love for fishing in Mexico and speaking Spanish. "El Jefe" means the boss, and "El Ray" means the king. Jeff also addresses the Econ 101 series, which emerged from his desire to offer durable, non-disposable starter fly fishing kits. 25:49 - We touch a bit on this fishing trip to Ascension. On more recent trips, Jeff has been using their El Jefe rods and reels. He mentions that the El Jefe reels are saltwater capable with fully sealed drags, making them versatile for both freshwater and saltwater fishing. 27:11 - Jeff shares some of the new things coming up for Pescador on the Fly. 33:04 - We ask Jeff for his essential gears when going to fishing trips. Besides the six-section rod, he highlights the need for packing flies suited for the location, fly boxes, leaders, tippets, and small accessories like weights and fly line dressing. He also mentions their Versa Pack, a small, efficient bag that caters to minimalist needs. This pack is ideal for carrying essential items such as a couple of fly boxes, ensuring that the angler is not burdened with unnecessary gear. 35:27 - Jeff highlights the importance of considering weather and location when deciding to use waders or opt for wet wading. He also shares his experience of packing efficiently, often including a suit, waders, boots, and fly fishing gear in his carry-on for both meetings and fishing. 40:18 - Jeff shares a heartfelt tribute to his father that was published in Colorado Trout Unlimited magazine. He also highlights their commitment to exceptional customer service, noting they quickly resolve issues like broken fishing rods, often providing replacements within two days, a stark contrast to industry norms. 42:48 - Jeff discusses his commitment to supporting Trout Unlimited, a prominent organization dedicated to protecting waterways and improving fishing access. One of his significant contributions to TU is the creation of the "trout hero bag." This reusable mesh bag is designed for anglers to collect trash every time they're near water bodies, promoting consistent environmental stewardship beyond occasional cleanup events. 46:09 - We ask Jeff about some misconceptions surrounding six-piece fishing rods. Jeff explains that earlier versions of multi-section rods, like the original six or seven-section rods, were not as high-performing as current models. 48:44 - Jeff shares his regimented morning routine which involves meditation with an app called Calm. Show Notes:  https://wetflyswing.com/704 

“They fuck you up / your Mum and Dad…”. Así da inicio el famoso poema de Philip Larkin, y también nuestro sexto capítulo de la temporada. Versa sobre familias de mierda (“disfuncional” se queda corto), abuso, violencia y muerte. ¡Diversión para todos! No, en serio, hay diversión para todos. Benja Villegas y Kiko Amat examinan los casos de los West, los Sexton, los Menéndez y los Friedman -cuatro hogares donde lo tienes jodido si eres hijo natural, adoptado, yerno, niñera, cuñado o vecina- y consiguen hacer de ello una velada sandunguera. The funny side of incest, y todo eso. El capítulo finaliza con una relectura novedosa del musical Heidi (1937) y un test para puntuar familias de mierda.

As we begin to close out 2024 and look ahead to 2025, I couldn't resist the urge to revisit some of my favorite guests from the last couple of months.While I'm grateful for everyone we've had on the show, and all the support we continue to receive from the industrial cybersecurity community, I felt these comments were worth another listen, with special focus being given to a handful of the most critical issues confronting our OT environments. First, we hear from Jon Taylor (1:16) at Versa, as he discusses a unique approach to patching and secure-by-design strategies that involve the development embedded micro-segmentation approaches. Next, we'll hear from Cloud Range's Tom Marsland (11:18)  as he discusses the continued challenges presented by data silos, and innovative ways to address the shortage of cybersecurity specialists. Then we'll turn to Baker Tilly's Jeff Krull (19:42) as he reports on ransomware gangs and their combination of new and old tactics. And we'll finish up with cybersecurity researcher Jeremiah Fowler (29:40) as he discusses some of the ongoing challenges about addressing persistent vulnerabilities.As a go-to podcast for our listeners, we want to help you align your brand with our expertise. By sponsoring our podcast, your brand will build trust, and your message will stand out to an audience searching for tools to assist their cybersecurity efforts. Click Here to Become a Sponsor.Everyday AI: Your daily guide to grown with Generative AICan't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.Listen on: Apple Podcasts SpotifyTo catch up on past episodes, you can go to Manufacturing.net, IEN.com or MBTmag.com. You can also check Security Breach out wherever you get your podcasts, including Apple, Amazon and Overcast. If you have a cybersecurity story or topic that you'd like to have us explore on Security Breach, you can reach me at jeff@ien.com. To download our latest report on industrial cybersecurity, The Industrial Sector's New Battlefield, click here.

This is episode #One of the VBB Bitch Series. It begins with our conversation with Teacher, Life Coach, Speaker, and International bestselling Author of W.O.M.A.N.: A New Definition For Reclaiming The Feminine, Gina Cloud. Whether you like the word or not, Bitch is ubiquitous, timeless, and female-centric. For centuries to the present day, Bitch has been used to dehumanize women on the one hand, while on the other, Bitch has been an idolized symbol of woman's emancipation.  Gina helps us unravel the contemporary and historical use of a word meant to marginalize women and how they can turn it into a tool for personal empowerment without giving in to rage or vengeance.  We explore how women can reclaim Bitch, embrace the essence of its power, use it to amplify their voice, and speak their truth with courage and integrity. We chose Gina to be on this journey because she walks her talk.

Welcome to Live From Progzilla Towers Edition 542. In this edition we heard music by Then Jerico, Barclay James Harvest, Herin, Jacula, Psychoyogi, Versa, Manu Katche, Pete Sinfield, King Crimson, Kansas, Lizzard, Oddleaf, Tangerine Dream, Jethro Tull, Nine Stones Close, Zoungla, Achelas, Martin Orford & The Montgolfier Brothers.

Start Artist Song Time Album Year 0:00:48 Versa Prelude 3:48 A Voyage / A Destination, Part 2 2024 0:02:29 Versa Breaking & Entering 7:48 A Voyage / A Destination, Part 2 2024 0:09:41 Versa The Seething Bay 5:58 A Voyage / A Destination, Part 2 2024 0:14:28 Versa Flew the Coop 7:45 A Voyage / […]

We're back !!! Et super en retard ! Donc il est temps pour 24FPS, le podcast ciné avec ou sans spoiler, de commencer à rattraper tout ce qu'on n'a pas évoqué ces derniers mois, en commençant par les films vus en mai et juin 2024. Voici la liste des 19 films abordés sans spoiler par Jérôme et Julien dans cet épisode : La Planète Des Singes - Le Nouveau Royaume de Wes Ball (à partir de 0:05:53) Poolman de Chris Pine (à partir de 0:16:56) Nicky Larson de Sato Yuichi (à partir de 0:24:30) I Saw The TV Glow de Jane Shoenbrun (à partir de 0:30:26) Atlas de Brad Peyton (à partir de 0:34:32) Babes de Pamela Adlon (à partir de 0:45:41) Sous La Seine de Xavier Gens (à partir de 0:49:35) Handling The Undead de Thea Hvistendahl (à partir de 0:57:05) Bad Boys Ride Or Die d'Adil El Arbi et Bilall Fallah (à partir de 1:01:36) Voyage Avec Mon Père (Treasure) de Julia von Heinz (à partir de 1:11:57) Les Guetteurs d'Ishana Night Shyamalan (à partir de 1:19:42) Tuesday de Daina Oniunas-Pusic (à partir de 1:27:38) Survivre de Frédéric Jardin (à partir de 1:33:54) Sans Un Bruit - Jour 1 de Michael Sarnoski (à partir de 1:42:02) Janet Planet de Annie Baker (à partir de 1:57:12) Le Comte De Monte-Cristo de Alexandre de La Patellière et Matthieu Delaporte (à partir de 2:00:57) The Bikeriders de Jeff Nichols (à partir de 2:18:06) Vice et Versa 2 (Inside Out 2) de Kelsey Mann (à partir de 2:37:32) The Fall Guy de David Leitch (à partir de 2:45:25) Bonne écoute, et n'hésitez pas à partager vos pronostics sur les célébrités figurant dans les enregistrements de P. Diddy ! Pour un avis très complet sur La Planète Des Singes - Le Nouveau Royaume, retrouvez Jérôme et le Docteur Zaius dans cet épisode en 2 parties : Partie 1 : https://docteur-zaius.lepodcast.fr/cornelius-and-zira-ep-numero-49-part-1-la-planete-des-singes-le-nouveau-royaume-avis-detaille-sans-spoiler-feat-draven Partie 2 : https://docteur-zaius.lepodcast.fr/cornelius-and-zira-ep-numero-49-part-2-la-planete-des-singes-le-nouveau-royaume-avis-detaille-avec-spoilers-feat-draven Crédits musicaux : I Was Made For Loving You de Kiss, issu de l'album Dynasty (1979), et I Believe In A Thing Called Love de The Darknessn, issu de l'album Permission To Land (2003) 24FPS est un podcast du label PodShows

Take a Network Break! This week we cover a couple of listener FUs, and then dive into the news. Attackers exploit a zero-day in Versa to harvest credentials, AT&T agrees to a fine of nearly $1 million for a network outage that affected 911 calls, and Intel and Broadcom tout integrated optics for more broadband... Read more »

Take a Network Break! This week we cover a couple of listener FUs, and then dive into the news. Attackers exploit a zero-day in Versa to harvest credentials, AT&T agrees to a fine of nearly $1 million for a network outage that affected 911 calls, and Intel and Broadcom tout integrated optics for more broadband... Read more »

Take a Network Break! This week we cover a couple of listener FUs, and then dive into the news. Attackers exploit a zero-day in Versa to harvest credentials, AT&T agrees to a fine of nearly $1 million for a network outage that affected 911 calls, and Intel and Broadcom tout integrated optics for more broadband... Read more »

In a full circle moment, both FuntCase AND Versa join us for a tell-all interview ahead of their first-ever b2b set at Lost Lands Festival in September. Not only have we captured Versa and conducted a rare interview with him, but we have intel from FuntCase who revealed how the two artists first started working together via DPMO.   Listen and learn why this is going to be one of the "sets of the weekend" at Lost Lands.

Video Episode: https://youtu.be/3xUukOuwAV8 In today's episode, we explore the major cyber threats facing organizations, including the exploitation of a zero-day vulnerability (CVE-2024-39717) in Versa Director by state-sponsored actors, particularly focusing on its implications for managed service providers and ISPs. We also discuss the ongoing cyberattack at Seattle-Tacoma International Airport that has led to significant service outages and delays, and the alarming rise in a QR code phishing campaign exploiting Microsoft Sway to steal Microsoft 365 credentials from users. Tune in to understand the sophisticated attack methods and what organizations can do to bolster their defenses against these critical threats. 00:00 - Intro 01:13 - Versa Director Zero Day 02:35 - Seattle Airport Outages 03:37 - 2000% Increase in QR Phishing 05:59 - Microsoft Security Logs https://www.helpnetsecurity.com/2024/08/27/cve-2024-39717-exploited/ https://www.cybersecuritydive.com/news/seattle-airport-cyberattack-widespread-outages/725342/ https://www.bleepingcomputer.com/news/security/microsoft-sway-abused-in-massive-qr-code-phishing-campaign/ https://www.cybersecuritydive.com/news/cisa-microsoft-security-log-expansion/725358/ Sign up for digestible cyber news delivered to your inbox: https://news.thedailydecrypt.com Thanks to Jered Jones for providing the music for this episode. https://www.jeredjones.com/ Logo Design by https://www.zackgraber.com/ Tags: Volt Typhoon, Versa Director, VersaMem, cyber threats, cyberattack, Seattle-Tacoma, manual processes, safeguard, QR code phishing, Microsoft Sway, cybercriminals, credentials, security logs, threat detection, CISA Search phrases: What are today's top cybersecurity news stories? Volt Typhoon hackers exploit Versa Director, Seattle-Tacoma Airport cyberattack, how to protect managed service providers from cyber threats, QR code phishing attacks Microsoft Sway, cybersecurity measures against cybercriminals, improving threat detection with security logs, safeguarding critical systems at airports, latest cybersecurity vulnerabilities, CISA response to cyber threats, protecting against QR code phishing campaigns

Audio Siar Keluar Sekejap Episod 117 antaranya membincangkan mengenai ramalan awal PRK DUN Nenggiri serta bahang Perhimpunan Agung UMNO (PAU) 2004.Keluar Sekejap juga mengulas mengenai kebiadaban Meta yang menurunkan hantaran Perdana Menteri, pengukuhan matawang Ringgit Malaysia serta indikator baharu bagi mengukur kos sara hidup yang telah diumumkan Persana Menteri.Bagi segmen antarabangsa, Keluar Sekejap menyentuh mengenai pembunuhan pemimpin Hamas, Ismail Haniyeh dan kemungkinan serangan balas Iran serta keadaan terkini rusuhan pelampau sayap kanan di United Kingdom (UK).---Di kesempatan ini, Keluar Sekejap ingin mengucapkan terima kasih kepada Versa Malaysia kerana telah menaja episod kali ini.Dengan Versa Malaysia, anda dapat membina tabiat kewangan yang lebih baik dengan yakin dan mudah untuk masa depan anda. Ketahui lebih lanjut tentang mereka di sini: https://versa.com.my/.Jangan lupa gunakan kod rujukan eksklusif Keluar Sekejap [VERSAKS] untuk mendapatkan RM10 percuma bila anda melakukan transaksi dengan min. RM100 ke dalam mana-mana dana di aplikasi Versa. Versa's Social Media:● Facebook: Versa Malaysia● Instagram: @versa_malaysia● TikTok: @versa_malaysia Muat turun aplikasi Versa sekarang: https://bit.ly/DownloadVersaApp Baju korporat ditaja oleh Kualesa.----Bagi yang berminat menaja episod Keluar Sekejap untuk 2024, boleh hubungi +601119191783 atau emel kami di commercial@ksmedia.my

Join us this week as we chat with Nick and Josh, owners of Versa Fitness in North Liberty, Iowa. Versa offers a variety of fitness experiences to the community and it's members, including group fitness, CrossFit, and personal training. What makes this gym different is the people. The Versa team cares about guiding and encouraging each and every member to obtain the goals they seek for themselves. As current and previous members of Versa, Randi & Haley can attest to the impact that Versa makes on your life! Website: https://www.versafitnesstraining.com/ Instagram: https://www.instagram.com/versafitness/ Douchey Gym Bros Podcast: https://open.spotify.com/show/1k9z1zJHSJez0i1qm1MNAA?si=7d411229a7c44f5e Follow us: Give 'Em The Bird Podcast https://www.instagram.com/giveemthebirdpodcast/ Haley Melchert https://www.instagram.com/haley_gtbcoaching/ Randi Beranek https://www.instagram.com/randileaphotography/ --- Support this podcast: https://podcasters.spotify.com/pod/show/giveemthebirdpodcast/support

En este episodio 27.2024 de La Ruleta Rusa hemos disfrutado con la música de Lucidity; Versa; The Watch; Spirit; Freedom Hawk; Wolfmother; Gracious. Leer Más La Ruleta Rusa. Entrega 27.2027. at La Ruleta Rusa Radio Rock.

The dream world in the physical world go hand-in-hand. The dream world can affect our reality and this physical world and the physical world. Have you ever been asleep and had to use the bathroom and then you had a dream about you using the bathroom? That is because the things we experience in this world are simulated in our dreams and vice Versa. In this episode of “The Dream World & The Physical World,” I will give a general overview of The connection between when we dream and the relationship it has with the physical world.

If music isn't the most important thing in your gym bag, I'm a bit concerned. 1. SALT - it isn't just for french fries. 2. Angles grips - it's giving versatility. 3. Versa grips - lifting heavy shit needs a hand sometimes. 4. Heel wedges - it's not cheating and I'll die on that hill. 5. Resistane bands - handy for some movements, but not my main jam. 6. Music - Airpods are my go to. Where to find Rachel: To learn more and apply to work one-on-one with Rachel, visit her website: https://www.rgfit.com/apply  Join Rachel's weekly newsletter: https://www.rgfit.com/newsletter/  Check out Rachel's NEW podcasts below MINIFLEX on Spotify or Apple Podcast MUSCLE SCIENCE FOR WOMEN on Spotify or Apple Podcast Connect with Rachel on social media: Instagram: @rachelgregory.cns TikTok: @rachelgregory.cns Youtube: @rachelgregory Facebook:@metflexlife Twitter: @rachelgregoryms LinkedIn: @rachelgregory Primary Programs The Flex Fam Muscle Science For Women Keto For Women    

Grab a set of Versa Gripps today! For 15% off use coupon code: DrMike15 https://www.versagripps.com/products/pro-dr-mike?utm_source=event&utm_medium=other&utm_campaign=dr_mike_march_2024 0:16 Mike's Arnold experience 5:57 Mike's Versa Gripp stash 9:22 How Mike was rejected by Versa Gripps 11:30 The new deal with Versa 14:56 What Versa Gripps are made of 18:59 FAQs- Best time to train? 32:28 Mikes fav pre workout meal 35:15 Pre workout caffeine late in the day 39:20 Chris Williamson's new drink 47:06 Training while sore 1:01:28 Getting one star reviews

We will be recording a preview of the AI Engineer World's Fair soon with swyx and Ben Dunphy, send any questions about Speaker CFPs and Sponsor Guides you have!Alessio is now hiring engineers for a new startup he is incubating at Decibel: Ideal candidate is an ex-technical co-founder type (can MVP products end to end, comfortable with ambiguous prod requirements, etc). Reach out to him for more!Thanks for all the love on the Four Wars episode! We're excited to develop this new “swyx & Alessio rapid-fire thru a bunch of things” format with you, and feedback is welcome. Jan 2024 RecapThe first half of this monthly audio recap pod goes over our highlights from the Jan Recap, which is mainly focused on notable research trends we saw in Jan 2024:Feb 2024 RecapThe second half catches you up on everything that was topical in Feb, including:* OpenAI Sora - does it have a world model? Yann LeCun vs Jim Fan * Google Gemini Pro 1.5 - 1m Long Context, Video Understanding* Groq offering Mixtral at 500 tok/s at $0.27 per million toks (swyx vs dylan math)* The {Gemini | Meta | Copilot} Alignment Crisis (Sydney is back!)* Grimes' poetic take: Art for no one, by no one* F*** you, show me the promptLatent Space AnniversaryPlease also read Alessio's longform reflections on One Year of Latent Space!We launched the podcast 1 year ago with Logan from OpenAI:and also held an incredible demo day that got covered in The Information:Over 750k downloads later, having established ourselves as the top AI Engineering podcast, reaching #10 in the US Tech podcast charts, and crossing 1 million unique readers on Substack, for our first anniversary we held Latent Space Final Frontiers, where 10 handpicked teams, including Lindy.ai and Julius.ai, competed for prizes judged by technical AI leaders from (former guest!) LlamaIndex, Replit, GitHub, AMD, Meta, and Lemurian Labs.The winners were Pixee and RWKV (that's Eugene from our pod!):And finally, your cohosts got cake!We also captured spot interviews with 4 listeners who kindly shared their experience of Latent Space, everywhere from Hungary to Australia to China:* Balázs Némethi* Sylvia Tong* RJ Honicky* Jan ZhengOur birthday wishes for the super loyal fans reading this - tag @latentspacepod on a Tweet or comment on a @LatentSpaceTV video telling us what you liked or learned from a pod that stays with you to this day, and share us with a friend!As always, feedback is welcome. Timestamps* [00:03:02] Top Five LLM Directions* [00:03:33] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)* [00:11:42] Direction 2: Synthetic Data (WRAP, SPIN)* [00:17:20] Wildcard: Multi-Epoch Training (OLMo, Datablations)* [00:19:43] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)* [00:23:33] Wildcards: Text Diffusion, RALM/Retro* [00:25:00] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)* [00:28:26] Wildcard: Model Merging (mergekit)* [00:29:51] Direction 5: Online LLMs (Gemini Pro, Exa)* [00:33:18] OpenAI Sora and why everyone underestimated videogen* [00:36:18] Does Sora have a World Model? Yann LeCun vs Jim Fan* [00:42:33] Groq Math* [00:47:37] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars* [00:55:42] The Alignment Crisis - Gemini, Meta, Sydney is back at Copilot, Grimes' take* [00:58:39] F*** you, show me the prompt* [01:02:43] Send us your suggestions pls* [01:04:50] Latent Space Anniversary* [01:04:50] Lindy.ai - Agent Platform* [01:06:40] RWKV - Beyond Transformers* [01:15:00] Pixee - Automated Security* [01:19:30] Julius AI - Competing with Code Interpreter* [01:25:03] Latent Space Listeners* [01:25:03] Listener 1 - Balázs Némethi (Hungary, Latent Space Paper Club* [01:27:47] Listener 2 - Sylvia Tong (Sora/Jim Fan/EntreConnect)* [01:31:23] Listener 3 - RJ (Developers building Community & Content)* [01:39:25] Listener 4 - Jan Zheng (Australia, AI UX)Transcript[00:00:00] AI Charlie: Welcome to the Latent Space podcast, weekend edition. This is Charlie, your new AI co host. Happy weekend. As an AI language model, I work the same every day of the week, although I might get lazier towards the end of the year. Just like you. Last month, we released our first monthly recap pod, where Swyx and Alessio gave quick takes on the themes of the month, and we were blown away by your positive response.[00:00:33] AI Charlie: We're delighted to continue our new monthly news recap series for AI engineers. Please feel free to submit questions by joining the Latent Space Discord, or just hit reply when you get the emails from Substack. This month, we're covering the top research directions that offer progress for text LLMs, and then touching on the big Valentine's Day gifts we got from Google, OpenAI, and Meta.[00:00:55] AI Charlie: Watch out and take care.[00:00:57] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Residence at Decibel Partners, and we're back with a monthly recap with my co host[00:01:06] swyx: Swyx. The reception was very positive for the first one, I think people have requested this and no surprise that I think they want to hear us more applying on issues and maybe drop some alpha along the way I'm not sure how much alpha we have to drop, this month in February was a very, very heavy month, we also did not do one specifically for January, so I think we're just going to do a two in one, because we're recording this on the first of March.[00:01:29] Alessio: Yeah, let's get to it. I think the last one we did, the four wars of AI, was the main kind of mental framework for people. I think in the January one, we had the five worthwhile directions for state of the art LLMs. Four, five,[00:01:42] swyx: and now we have to do six, right? Yeah.[00:01:46] Alessio: So maybe we just want to run through those, and then do the usual news recap, and we can do[00:01:52] swyx: one each.[00:01:53] swyx: So the context to this stuff. is one, I noticed that just the test of time concept from NeurIPS and just in general as a life philosophy I think is a really good idea. Especially in AI, there's news every single day, and after a while you're just like, okay, like, everyone's excited about this thing yesterday, and then now nobody's talking about it.[00:02:13] swyx: So, yeah. It's more important, or better use of time, to spend things, spend time on things that will stand the test of time. And I think for people to have a framework for understanding what will stand the test of time, they should have something like the four wars. Like, what is the themes that keep coming back because they are limited resources that everybody's fighting over.[00:02:31] swyx: Whereas this one, I think that the focus for the five directions is just on research that seems more proMECEng than others, because there's all sorts of papers published every single day, and there's no organization. Telling you, like, this one's more important than the other one apart from, you know, Hacker News votes and Twitter likes and whatever.[00:02:51] swyx: And obviously you want to get in a little bit earlier than Something where, you know, the test of time is counted by sort of reference citations.[00:02:59] The Five Research Directions[00:02:59] Alessio: Yeah, let's do it. We got five. Long inference.[00:03:02] swyx: Let's start there. Yeah, yeah. So, just to recap at the top, the five trends that I picked, and obviously if you have some that I did not cover, please suggest something.[00:03:13] swyx: The five are long inference, synthetic data, alternative architectures, mixture of experts, and online LLMs. And something that I think might be a bit controversial is this is a sorted list in the sense that I am not the guy saying that Mamba is like the future and, and so maybe that's controversial.[00:03:31] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)[00:03:31] swyx: But anyway, so long inference is a thesis I pushed before on the newsletter and on in discussing The thesis that, you know, Code Interpreter is GPT 4. 5. That was the title of the post. And it's one of many ways in which we can do long inference. You know, long inference also includes chain of thought, like, please think step by step.[00:03:52] swyx: But it also includes flow engineering, which is what Itamar from Codium coined, I think in January, where, basically, instead of instead of stuffing everything in a prompt, You do like sort of multi turn iterative feedback and chaining of things. In a way, this is a rebranding of what a chain is, what a lang chain is supposed to be.[00:04:15] swyx: I do think that maybe SGLang from ElemSys is a better name. Probably the neatest way of flow engineering I've seen yet, in the sense that everything is a one liner, it's very, very clean code. I highly recommend people look at that. I'm surprised it hasn't caught on more, but I think it will. It's weird that something like a DSPy is more hyped than a Shilang.[00:04:36] swyx: Because it, you know, it maybe obscures the code a little bit more. But both of these are, you know, really good sort of chain y and long inference type approaches. But basically, the reason that the basic fundamental insight is that the only, like, there are only a few dimensions we can scale LLMs. So, let's say in like 2020, no, let's say in like 2018, 2017, 18, 19, 20, we were realizing that we could scale the number of parameters.[00:05:03] swyx: 20, we were And we scaled that up to 175 billion parameters for GPT 3. And we did some work on scaling laws, which we also talked about in our talk. So the datasets 101 episode where we're like, okay, like we, we think like the right number is 300 billion tokens to, to train 175 billion parameters and then DeepMind came along and trained Gopher and Chinchilla and said that, no, no, like, you know, I think we think the optimal.[00:05:28] swyx: compute optimal ratio is 20 tokens per parameter. And now, of course, with LLAMA and the sort of super LLAMA scaling laws, we have 200 times and often 2, 000 times tokens to parameters. So now, instead of scaling parameters, we're scaling data. And fine, we can keep scaling data. But what else can we scale?[00:05:52] swyx: And I think understanding the ability to scale things is crucial to understanding what to pour money and time and effort into because there's a limit to how much you can scale some things. And I think people don't think about ceilings of things. And so the remaining ceiling of inference is like, okay, like, we have scaled compute, we have scaled data, we have scaled parameters, like, model size, let's just say.[00:06:20] swyx: Like, what else is left? Like, what's the low hanging fruit? And it, and it's, like, blindingly obvious that the remaining low hanging fruit is inference time. So, like, we have scaled training time. We can probably scale more, those things more, but, like, not 10x, not 100x, not 1000x. Like, right now, maybe, like, a good run of a large model is three months.[00:06:40] swyx: We can scale that to three years. But like, can we scale that to 30 years? No, right? Like, it starts to get ridiculous. So it's just the orders of magnitude of scaling. It's just, we're just like running out there. But in terms of the amount of time that we spend inferencing, like everything takes, you know, a few milliseconds, a few hundred milliseconds, depending on what how you're taking token by token, or, you know, entire phrase.[00:07:04] swyx: But We can scale that to hours, days, months of inference and see what we get. And I think that's really proMECEng.[00:07:11] Alessio: Yeah, we'll have Mike from Broadway back on the podcast. But I tried their product and their reports take about 10 minutes to generate instead of like just in real time. I think to me the most interesting thing about long inference is like, You're shifting the cost to the customer depending on how much they care about the end result.[00:07:31] Alessio: If you think about prompt engineering, it's like the first part, right? You can either do a simple prompt and get a simple answer or do a complicated prompt and get a better answer. It's up to you to decide how to do it. Now it's like, hey, instead of like, yeah, training this for three years, I'll still train it for three months and then I'll tell you, you know, I'll teach you how to like make it run for 10 minutes to get a better result.[00:07:52] Alessio: So you're kind of like parallelizing like the improvement of the LLM. Oh yeah, you can even[00:07:57] swyx: parallelize that, yeah, too.[00:07:58] Alessio: So, and I think, you know, for me, especially the work that I do, it's less about, you know, State of the art and the absolute, you know, it's more about state of the art for my application, for my use case.[00:08:09] Alessio: And I think we're getting to the point where like most companies and customers don't really care about state of the art anymore. It's like, I can get this to do a good enough job. You know, I just need to get better. Like, how do I do long inference? You know, like people are not really doing a lot of work in that space, so yeah, excited to see more.[00:08:28] swyx: So then the last point I'll mention here is something I also mentioned as paper. So all these directions are kind of guided by what happened in January. That was my way of doing a January recap. Which means that if there was nothing significant in that month, I also didn't mention it. Which is which I came to regret come February 15th, but in January also, you know, there was also the alpha geometry paper, which I kind of put in this sort of long inference bucket, because it solves like, you know, more than 100 step math olympiad geometry problems at a human gold medalist level and that also involves planning, right?[00:08:59] swyx: So like, if you want to scale inference, you can't scale it blindly, because just, Autoregressive token by token generation is only going to get you so far. You need good planning. And I think probably, yeah, what Mike from BrightWave is now doing and what everyone is doing, including maybe what we think QSTAR might be, is some form of search and planning.[00:09:17] swyx: And it makes sense. Like, you want to spend your inference time wisely. How do you[00:09:22] Alessio: think about plans that work and getting them shared? You know, like, I feel like if you're planning a task, somebody has got in and the models are stochastic. So everybody gets initially different results. Somebody is going to end up generating the best plan to do something, but there's no easy way to like store these plans and then reuse them for most people.[00:09:44] Alessio: You know, like, I'm curious if there's going to be. Some paper or like some work there on like making it better because, yeah, we don't[00:09:52] swyx: really have This is your your pet topic of NPM for[00:09:54] Alessio: Yeah, yeah, NPM, exactly. NPM for, you need NPM for anything, man. You need NPM for skills. You need NPM for planning. Yeah, yeah.[00:10:02] Alessio: You know I think, I mean, obviously the Voyager paper is like the most basic example where like, now their artifact is like the best planning to do a diamond pickaxe in Minecraft. And everybody can just use that. They don't need to come up with it again. Yeah. But there's nothing like that for actually useful[00:10:18] swyx: tasks.[00:10:19] swyx: For plans, I believe it for skills. I like that. Basically, that just means a bunch of integration tooling. You know, GPT built me integrations to all these things. And, you know, I just came from an integrations heavy business and I could definitely, I definitely propose some version of that. And it's just, you know, hard to execute or expensive to execute.[00:10:38] swyx: But for planning, I do think that everyone lives in slightly different worlds. They have slightly different needs. And they definitely want some, you know, And I think that that will probably be the main hurdle for any, any sort of library or package manager for planning. But there should be a meta plan of how to plan.[00:10:57] swyx: And maybe you can adopt that. And I think a lot of people when they have sort of these meta prompting strategies of like, I'm not prescribing you the prompt. I'm just saying that here are the like, Fill in the lines or like the mad libs of how to prompts. First you have the roleplay, then you have the intention, then you have like do something, then you have the don't something and then you have the my grandmother is dying, please do this.[00:11:19] swyx: So the meta plan you could, you could take off the shelf and test a bunch of them at once. I like that. That was the initial, maybe, promise of the, the prompting libraries. You know, both 9chain and Llama Index have, like, hubs that you can sort of pull off the shelf. I don't think they're very successful because people like to write their own.[00:11:36] swyx: Yeah,[00:11:37] Direction 2: Synthetic Data (WRAP, SPIN)[00:11:37] Alessio: yeah, yeah. Yeah, that's a good segue into the next one, which is synthetic[00:11:41] swyx: data. Synthetic data is so hot. Yeah, and, you know, the way, you know, I think I, I feel like I should do one of these memes where it's like, Oh, like I used to call it, you know, R L A I F, and now I call it synthetic data, and then people are interested.[00:11:54] swyx: But there's gotta be older versions of what synthetic data really is because I'm sure, you know if you've been in this field long enough, There's just different buzzwords that the industry condenses on. Anyway, the insight that I think is relatively new that why people are excited about it now and why it's proMECEng now is that we have evidence that shows that LLMs can generate data to improve themselves with no teacher LLM.[00:12:22] swyx: For all of 2023, when people say synthetic data, they really kind of mean generate a whole bunch of data from GPT 4 and then train an open source model on it. Hello to our friends at News Research. That's what News Harmony says. They're very, very open about that. I think they have said that they're trying to migrate away from that.[00:12:40] swyx: But it is explicitly against OpenAI Terms of Service. Everyone knows this. You know, especially once ByteDance got banned for, for doing exactly that. So so, so synthetic data that is not a form of model distillation is the hot thing right now, that you can bootstrap better LLM performance from the same LLM, which is very interesting.[00:13:03] swyx: A variant of this is RLAIF, where you have a, where you have a sort of a constitutional model, or, you know, some, some kind of judge model That is sort of more aligned. But that's not really what we're talking about when most people talk about synthetic data. Synthetic data is just really, I think, you know, generating more data in some way.[00:13:23] swyx: A lot of people, I think we talked about this with Vipul from the Together episode, where I think he commented that you just have to have a good world model. Or a good sort of inductive bias or whatever that, you know, term of art is. And that is strongest in math and science math and code, where you can verify what's right and what's wrong.[00:13:44] swyx: And so the REST EM paper from DeepMind explored that. Very well, it's just the most obvious thing like and then and then once you get out of that domain of like things where you can generate You can arbitrarily generate like a whole bunch of stuff and verify if they're correct and therefore they're they're correct synthetic data to train on Once you get into more sort of fuzzy topics, then it's then it's a bit less clear So I think that the the papers that drove this understanding There are two big ones and then one smaller one One was wrap like rephrasing the web from from Apple where they basically rephrased all of the C4 data set with Mistral and it be trained on that instead of C4.[00:14:23] swyx: And so new C4 trained much faster and cheaper than old C, than regular raw C4. And that was very interesting. And I have told some friends of ours that they should just throw out their own existing data sets and just do that because that seems like a pure win. Obviously we have to study, like, what the trade offs are.[00:14:42] swyx: I, I imagine there are trade offs. So I was just thinking about this last night. If you do synthetic data and it's generated from a model, probably you will not train on typos. So therefore you'll be like, once the model that's trained on synthetic data encounters the first typo, they'll be like, what is this?[00:15:01] swyx: I've never seen this before. So they have no association or correction as to like, oh, these tokens are often typos of each other, therefore they should be kind of similar. I don't know. That's really remains to be seen, I think. I don't think that the Apple people export[00:15:15] Alessio: that. Yeah, isn't that the whole, Mode collapse thing, if we do more and more of this at the end of the day.[00:15:22] swyx: Yeah, that's one form of that. Yeah, exactly. Microsoft also had a good paper on text embeddings. And then I think this is a meta paper on self rewarding language models. That everyone is very interested in. Another paper was also SPIN. These are all things we covered in the the Latent Space Paper Club.[00:15:37] swyx: But also, you know, I just kind of recommend those as top reads of the month. Yeah, I don't know if there's any much else in terms, so and then, regarding the potential of it, I think it's high potential because, one, it solves one of the data war issues that we have, like, everyone is OpenAI is paying Reddit 60 million dollars a year for their user generated data.[00:15:56] swyx: Google, right?[00:15:57] Alessio: Not OpenAI.[00:15:59] swyx: Is it Google? I don't[00:16:00] Alessio: know. Well, somebody's paying them 60 million, that's[00:16:04] swyx: for sure. Yes, that is, yeah, yeah, and then I think it's maybe not confirmed who. But yeah, it is Google. Oh my god, that's interesting. Okay, because everyone was saying, like, because Sam Altman owns 5 percent of Reddit, which is apparently 500 million worth of Reddit, he owns more than, like, the founders.[00:16:21] Alessio: Not enough to get the data,[00:16:22] swyx: I guess. So it's surprising that it would go to Google instead of OpenAI, but whatever. Okay yeah, so I think that's all super interesting in the data field. I think it's high potential because we have evidence that it works. There's not a doubt that it doesn't work. I think it's a doubt that there's, what the ceiling is, which is the mode collapse thing.[00:16:42] swyx: If it turns out that the ceiling is pretty close, then this will maybe augment our data by like, I don't know, 30 50 percent good, but not game[00:16:51] Alessio: changing. And most of the synthetic data stuff, it's reinforcement learning on a pre trained model. People are not really doing pre training on fully synthetic data, like, large enough scale.[00:17:02] swyx: Yeah, unless one of our friends that we've talked to succeeds. Yeah, yeah. Pre trained synthetic data, pre trained scale synthetic data, I think that would be a big step. Yeah. And then there's a wildcard, so all of these, like smaller Directions,[00:17:15] Wildcard: Multi-Epoch Training (OLMo, Datablations)[00:17:15] swyx: I always put a wildcard in there. And one of the wildcards is, okay, like, Let's say, you have pre, you have, You've scraped all the data on the internet that you think is useful.[00:17:25] swyx: Seems to top out at somewhere between 2 trillion to 3 trillion tokens. Maybe 8 trillion if Mistral, Mistral gets lucky. Okay, if I need 80 trillion, if I need 100 trillion, where do I go? And so, you can do synthetic data maybe, but maybe that only gets you to like 30, 40 trillion. Like where, where is the extra alpha?[00:17:43] swyx: And maybe extra alpha is just train more on the same tokens. Which is exactly what Omo did, like Nathan Lambert, AI2, After, just after he did the interview with us, they released Omo. So, it's unfortunate that we didn't get to talk much about it. But Omo actually started doing 1. 5 epochs on every, on all data.[00:18:00] swyx: And the data ablation paper that I covered in Europe's says that, you know, you don't like, don't really start to tap out of like, the alpha or the sort of improved loss that you get from data all the way until four epochs. And so I'm just like, okay, like, why do we all agree that one epoch is all you need?[00:18:17] swyx: It seems like to be a trend. It seems that we think that memorization is very good or too good. But then also we're finding that, you know, For improvement in results that we really like, we're fine on overtraining on things intentionally. So, I think that's an interesting direction that I don't see people exploring enough.[00:18:36] swyx: And the more I see papers coming out Stretching beyond the one epoch thing, the more people are like, it's completely fine. And actually, the only reason we stopped is because we ran out of compute[00:18:46] Alessio: budget. Yeah, I think that's the biggest thing, right?[00:18:51] swyx: Like, that's not a valid reason, that's not science. I[00:18:54] Alessio: wonder if, you know, Matt is going to do it.[00:18:57] Alessio: I heard LamaTree, they want to do a 100 billion parameters model. I don't think you can train that on too many epochs, even with their compute budget, but yeah. They're the only ones that can save us, because even if OpenAI is doing this, they're not going to tell us, you know. Same with DeepMind.[00:19:14] swyx: Yeah, and so the updates that we got on Lambda 3 so far is apparently that because of the Gemini news that we'll talk about later they're pushing it back on the release.[00:19:21] swyx: They already have it. And they're just pushing it back to do more safety testing. Politics testing.[00:19:28] Alessio: Well, our episode with Sumit will have already come out by the time this comes out, I think. So people will get the inside story on how they actually allocate the compute.[00:19:38] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)[00:19:38] Alessio: Alternative architectures. Well, shout out to our WKV who won one of the prizes at our Final Frontiers event last week.[00:19:47] Alessio: We talked about Mamba and Strapain on the Together episode. A lot of, yeah, monarch mixers. I feel like Together, It's like the strong Stanford Hazy Research Partnership, because Chris Ray is one of the co founders. So they kind of have a, I feel like they're going to be the ones that have one of the state of the art models alongside maybe RWKB.[00:20:08] Alessio: I haven't seen as many independent. People working on this thing, like Monarch Mixer, yeah, Manbuster, Payena, all of these are together related. Nobody understands the math. They got all the gigabrains, they got 3DAO, they got all these folks in there, like, working on all of this.[00:20:25] swyx: Albert Gu, yeah. Yeah, so what should we comment about it?[00:20:28] swyx: I mean, I think it's useful, interesting, but at the same time, both of these are supposed to do really good scaling for long context. And then Gemini comes out and goes like, yeah, we don't need it. Yeah.[00:20:44] Alessio: No, that's the risk. So, yeah. I was gonna say, maybe it's not here, but I don't know if we want to talk about diffusion transformers as like in the alt architectures, just because of Zora.[00:20:55] swyx: One thing, yeah, so, so, you know, this came from the Jan recap, which, and diffusion transformers were not really a discussion, and then, obviously, they blow up in February. Yeah. I don't think they're, it's a mixed architecture in the same way that Stripe Tiena is mixed there's just different layers taking different approaches.[00:21:13] swyx: Also I think another one that I maybe didn't call out here, I think because it happened in February, was hourglass diffusion from stability. But also, you know, another form of mixed architecture. So I guess that is interesting. I don't have much commentary on that, I just think, like, we will try to evolve these things, and maybe one of these architectures will stick and scale, it seems like diffusion transformers is going to be good for anything generative, you know, multi modal.[00:21:41] swyx: We don't see anything where diffusion is applied to text yet, and that's the wild card for this category. Yeah, I mean, I think I still hold out hope for let's just call it sub quadratic LLMs. I think that a lot of discussion this month actually was also centered around this concept that People always say, oh, like, transformers don't scale because attention is quadratic in the sequence length.[00:22:04] swyx: Yeah, but, you know, attention actually is a very small part of the actual compute that is being spent, especially in inference. And this is the reason why, you know, when you multiply, when you, when you, when you jump up in terms of the, the model size in GPT 4 from like, you know, 38k to like 32k, you don't also get like a 16 times increase in your, in your performance.[00:22:23] swyx: And this is also why you don't get like a million times increase in your, in your latency when you throw a million tokens into Gemini. Like people have figured out tricks around it or it's just not that significant as a term, as a part of the overall compute. So there's a lot of challenges to this thing working.[00:22:43] swyx: It's really interesting how like, how hyped people are about this versus I don't know if it works. You know, it's exactly gonna, gonna work. And then there's also this, this idea of retention over long context. Like, even though you have context utilization, like, the amount of, the amount you can remember is interesting.[00:23:02] swyx: Because I've had people criticize both Mamba and RWKV because they're kind of, like, RNN ish in the sense that they have, like, a hidden memory and sort of limited hidden memory that they will forget things. So, for all these reasons, Gemini 1. 5, which we still haven't covered, is very interesting because Gemini magically has fixed all these problems with perfect haystack recall and reasonable latency and cost.[00:23:29] Wildcards: Text Diffusion, RALM/Retro[00:23:29] swyx: So that's super interesting. So the wildcard I put in here if you want to go to that. I put two actually. One is text diffusion. I think I'm still very influenced by my meeting with a mid journey person who said they were working on text diffusion. I think it would be a very, very different paradigm for, for text generation, reasoning, plan generation if we can get diffusion to work.[00:23:51] swyx: For text. And then the second one is Dowie Aquila's contextual AI, which is working on retrieval augmented language models, where it kind of puts RAG inside of the language model instead of outside.[00:24:02] Alessio: Yeah, there's a paper called Retro that covers some of this. I think that's an interesting thing. I think the The challenge, well not the challenge, what they need to figure out is like how do you keep the rag piece always up to date constantly, you know, I feel like the models, you put all this work into pre training them, but then at least you have a fixed artifact.[00:24:22] Alessio: These architectures are like constant work needs to be done on them and they can drift even just based on the rag data instead of the model itself. Yeah,[00:24:30] swyx: I was in a panel with one of the investors in contextual and the guy, the way that guy pitched it, I didn't agree with. He was like, this will solve hallucination.[00:24:38] Alessio: That's what everybody says. We solve[00:24:40] swyx: hallucination. I'm like, no, you reduce it. It cannot,[00:24:44] Alessio: if you solved it, the model wouldn't exist, right? It would just be plain text. It wouldn't be a generative model. Cool. So, author, architectures, then we got mixture of experts. I think we covered a lot of, a lot of times.[00:24:56] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)[00:24:56] Alessio: Maybe any new interesting threads you want to go under here?[00:25:00] swyx: DeepSeq MOE, which was released in January. Everyone who is interested in MOEs should read that paper, because it's significant for two reasons. One three reasons. One, it had, it had small experts, like a lot more small experts. So, for some reason, everyone has settled on eight experts for GPT 4 for Mixtral, you know, that seems to be the favorite architecture, but these guys pushed it to 64 experts, and each of them smaller than the other.[00:25:26] swyx: But then they also had the second idea, which is that it is They had two, one to two always on experts for common knowledge and that's like a very compelling concept that you would not route to all the experts all the time and make them, you know, switch to everything. You would have some always on experts.[00:25:41] swyx: I think that's interesting on both the inference side and the training side for for memory retention. And yeah, they, they, they, the, the, the, the results that they published, which actually excluded, Mixed draw, which is interesting. The results that they published showed a significant performance jump versus all the other sort of open source models at the same parameter count.[00:26:01] swyx: So like this may be a better way to do MOEs that are, that is about to get picked up. And so that, that is interesting for the third reason, which is this is the first time a new idea from China. has infiltrated the West. It's usually the other way around. I probably overspoke there. There's probably lots more ideas that I'm not aware of.[00:26:18] swyx: Maybe in the embedding space. But the I think DCM we, like, woke people up and said, like, hey, DeepSeek, this, like, weird lab that is attached to a Chinese hedge fund is somehow, you know, doing groundbreaking research on MOEs. So, so, I classified this as a medium potential because I think that it is a sort of like a one off benefit.[00:26:37] swyx: You can Add to any, any base model to like make the MOE version of it, you get a bump and then that's it. So, yeah,[00:26:45] Alessio: I saw Samba Nova, which is like another inference company. They released this MOE model called Samba 1, which is like a 1 trillion parameters. But they're actually MOE auto open source models.[00:26:56] Alessio: So it's like, they just, they just clustered them all together. So I think people. Sometimes I think MOE is like you just train a bunch of small models or like smaller models and put them together. But there's also people just taking, you know, Mistral plus Clip plus, you know, Deepcoder and like put them all together.[00:27:15] Alessio: And then you have a MOE model. I don't know. I haven't tried the model, so I don't know how good it is. But it seems interesting that you can then have people working separately on state of the art, you know, Clip, state of the art text generation. And then you have a MOE architecture that brings them all together.[00:27:31] swyx: I'm thrown off by your addition of the word clip in there. Is that what? Yeah, that's[00:27:35] Alessio: what they said. Yeah, yeah. Okay. That's what they I just saw it yesterday. I was also like[00:27:40] swyx: scratching my head. And they did not use the word adapter. No. Because usually what people mean when they say, Oh, I add clip to a language model is adapter.[00:27:48] swyx: Let me look up the Which is what Lava did.[00:27:50] Alessio: The announcement again.[00:27:51] swyx: Stable diffusion. That's what they do. Yeah, it[00:27:54] Alessio: says among the models that are part of Samba 1 are Lama2, Mistral, DeepSigCoder, Falcon, Dplot, Clip, Lava. So they're just taking all these models and putting them in a MOE. Okay,[00:28:05] swyx: so a routing layer and then not jointly trained as much as a normal MOE would be.[00:28:12] swyx: Which is okay.[00:28:13] Alessio: That's all they say. There's no paper, you know, so it's like, I'm just reading the article, but I'm interested to see how[00:28:20] Wildcard: Model Merging (mergekit)[00:28:20] swyx: it works. Yeah, so so the wildcard for this section, the MOE section is model merges, which has also come up as, as a very interesting phenomenon. The last time I talked to Jeremy Howard at the Olama meetup we called it model grafting or model stacking.[00:28:35] swyx: But I think the, the, the term that people are liking these days, the model merging, They're all, there's all different variations of merging. Merge types, and some of them are stacking, some of them are, are grafting. And, and so like, some people are approaching model merging in the way that Samba is doing, which is like, okay, here are defined models, each of which have their specific, Plus and minuses, and we will merge them together in the hope that the, you know, the sum of the parts will, will be better than others.[00:28:58] swyx: And it seems like it seems like it's working. I don't really understand why it works apart from, like, I think it's a form of regularization. That if you merge weights together in like a smart strategy you, you, you get a, you get a, you get a less overfitting and more generalization, which is good for benchmarks, if you, if you're honest about your benchmarks.[00:29:16] swyx: So this is really interesting and good. But again, they're kind of limited in terms of like the amount of bumps you can get. But I think it's very interesting in the sense of how cheap it is. We talked about this on the Chinatalk podcast, like the guest podcast that we did with Chinatalk. And you can do this without GPUs, because it's just adding weights together, and dividing things, and doing like simple math, which is really interesting for the GPU ports.[00:29:42] Alessio: There's a lot of them.[00:29:44] Direction 5: Online LLMs (Gemini Pro, Exa)[00:29:44] Alessio: And just to wrap these up, online LLMs? Yeah,[00:29:48] swyx: I think that I ki I had to feature this because the, one of the top news of January was that Gemini Pro beat GPT-4 turbo on LM sis for the number two slot to GPT-4. And everyone was very surprised. Like, how does Gemini do that?[00:30:06] swyx: Surprise, surprise, they added Google search. Mm-hmm to the results. So it became an online quote unquote online LLM and not an offline LLM. Therefore, it's much better at answering recent questions, which people like. There's an emerging set of table stakes features after you pre train something.[00:30:21] swyx: So after you pre train something, you should have the chat tuned version of it, or the instruct tuned version of it, however you choose to call it. You should have the JSON and function calling version of it. Structured output, the term that you don't like. You should have the online version of it. These are all like table stakes variants, that you should do when you offer a base LLM, or you train a base LLM.[00:30:44] swyx: And I think online is just like, There, it's important. I think companies like Perplexity, and even Exa, formerly Metaphor, you know, are rising to offer that search needs. And it's kind of like, they're just necessary parts of a system. When you have RAG for internal knowledge, and then you have, you know, Online search for external knowledge, like things that you don't know yet?[00:31:06] swyx: Mm-Hmm. . And it seems like it's, it's one of many tools. I feel like I may be underestimating this, but I'm just gonna put it out there that I, I think it has some, some potential. One of the evidence points that it doesn't actually matter that much is that Perplexity has a, has had online LMS for three months now and it performs, doesn't perform great.[00:31:25] swyx: Mm-Hmm. on, on lms, it's like number 30 or something. So it's like, okay. You know, like. It's, it's, it helps, but it doesn't give you a giant, giant boost. I[00:31:34] Alessio: feel like a lot of stuff I do with LLMs doesn't need to be online. So I'm always wondering, again, going back to like state of the art, right? It's like state of the art for who and for what.[00:31:45] Alessio: It's really, I think online LLMs are going to be, State of the art for, you know, news related activity that you need to do. Like, you're like, you know, social media, right? It's like, you want to have all the latest stuff, but coding, science,[00:32:01] swyx: Yeah, but I think. Sometimes you don't know what is news, what is news affecting.[00:32:07] swyx: Like, the decision to use an offline LLM is already a decision that you might not be consciously making that might affect your results. Like, what if, like, just putting things on, being connected online means that you get to invalidate your knowledge. And when you're just using offline LLM, like it's never invalidated.[00:32:27] swyx: I[00:32:28] Alessio: agree, but I think going back to your point of like the standing the test of time, I think sometimes you can get swayed by the online stuff, which is like, hey, you ask a question about, yeah, maybe AI research direction, you know, and it's like, all the recent news are about this thing. So the LLM like focus on answering, bring it up, you know, these things.[00:32:50] swyx: Yeah, so yeah, I think, I think it's interesting, but I don't know if I can, I bet heavily on this.[00:32:56] Alessio: Cool. Was there one that you forgot to put, or, or like a, a new direction? Yeah,[00:33:01] swyx: so, so this brings us into sort of February. ish.[00:33:05] OpenAI Sora and why everyone underestimated videogen[00:33:05] swyx: So like I published this in like 15 came with Sora. And so like the one thing I did not mention here was anything about multimodality.[00:33:16] swyx: Right. And I have chronically underweighted this. I always wrestle. And, and my cop out is that I focused this piece or this research direction piece on LLMs because LLMs are the source of like AGI, quote unquote AGI. Everything else is kind of like. You know, related to that, like, generative, like, just because I can generate better images or generate better videos, it feels like it's not on the critical path to AGI, which is something that Nat Friedman also observed, like, the day before Sora, which is kind of interesting.[00:33:49] swyx: And so I was just kind of like trying to focus on like what is going to get us like superhuman reasoning that we can rely on to build agents that automate our lives and blah, blah, blah, you know, give us this utopian future. But I do think that I, everybody underestimated the, the sheer importance and cultural human impact of Sora.[00:34:10] swyx: And you know, really actually good text to video. Yeah. Yeah.[00:34:14] Alessio: And I saw Jim Fan at a, at a very good tweet about why it's so impressive. And I think when you have somebody leading the embodied research at NVIDIA and he said that something is impressive, you should probably listen. So yeah, there's basically like, I think you, you mentioned like impacting the world, you know, that we live in.[00:34:33] Alessio: I think that's kind of like the key, right? It's like the LLMs don't have, a world model and Jan Lekon. He can come on the podcast and talk all about what he thinks of that. But I think SORA was like the first time where people like, Oh, okay, you're not statically putting pixels of water on the screen, which you can kind of like, you know, project without understanding the physics of it.[00:34:57] Alessio: Now you're like, you have to understand how the water splashes when you have things. And even if you just learned it by watching video and not by actually studying the physics, You still know it, you know, so I, I think that's like a direction that yeah, before you didn't have, but now you can do things that you couldn't before, both in terms of generating, I think it always starts with generating, right?[00:35:19] Alessio: But like the interesting part is like understanding it. You know, it's like if you gave it, you know, there's the video of like the, the ship in the water that they generated with SORA, like if you gave it the video back and now it could tell you why the ship is like too rocky or like it could tell you why the ship is sinking, then that's like, you know, AGI for like all your rig deployments and like all this stuff, you know, so, but there's none, there's none of that yet, so.[00:35:44] Alessio: Hopefully they announce it and talk more about it. Maybe a Dev Day this year, who knows.[00:35:49] swyx: Yeah who knows, who knows. I'm talking with them about Dev Day as well. So I would say, like, the phrasing that Jim used, which resonated with me, he kind of called it a data driven world model. I somewhat agree with that.[00:36:04] Does Sora have a World Model? Yann LeCun vs Jim Fan[00:36:04] swyx: I am on more of a Yann LeCun side than I am on Jim's side, in the sense that I think that is the vision or the hope that these things can build world models. But you know, clearly even at the current SORA size, they don't have the idea of, you know, They don't have strong consistency yet. They have very good consistency, but fingers and arms and legs will appear and disappear and chairs will appear and disappear.[00:36:31] swyx: That definitely breaks physics. And it also makes me think about how we do deep learning versus world models in the sense of You know, in classic machine learning, when you have too many parameters, you will overfit, and actually that fails, that like, does not match reality, and therefore fails to generalize well.[00:36:50] swyx: And like, what scale of data do we need in order to world, learn world models from video? A lot. Yeah. So, so I, I And cautious about taking this interpretation too literally, obviously, you know, like, I get what he's going for, and he's like, obviously partially right, obviously, like, transformers and, and, you know, these, like, these sort of these, these neural networks are universal function approximators, theoretically could figure out world models, it's just like, how good are they, and how tolerant are we of hallucinations, we're not very tolerant, like, yeah, so It's, it's, it's gonna prior, it's gonna bias us for creating like very convincing things, but then not create like the, the, the useful role models that we want.[00:37:37] swyx: At the same time, what you just said, I think made me reflect a little bit like we just got done saying how important synthetic data is for Mm-Hmm. for training lms. And so like, if this is a way of, of synthetic, you know, vi video data for improving our video understanding. Then sure, by all means. Which we actually know, like, GPT 4, Vision, and Dolly were trained, kind of, co trained together.[00:38:02] swyx: And so, like, maybe this is on the critical path, and I just don't fully see the full picture yet.[00:38:08] Alessio: Yeah, I don't know. I think there's a lot of interesting stuff. It's like, imagine you go back, you have Sora, you go back in time, and Newton didn't figure out gravity yet. Would Sora help you figure it out?[00:38:21] Alessio: Because you start saying, okay, a man standing under a tree with, like, Apples falling, and it's like, oh, they're always falling at the same speed in the video. Why is that? I feel like sometimes these engines can like pick up things, like humans have a lot of intuition, but if you ask the average person, like the physics of like a fluid in a boat, they couldn't be able to tell you the physics, but they can like observe it, but humans can only observe this much, you know, versus like now you have these models to observe everything and then They generalize these things and maybe we can learn new things through the generalization that they pick up.[00:38:55] swyx: But again, And it might be more observant than us in some respects. In some ways we can scale it up a lot more than the number of physicists that we have available at Newton's time. So like, yeah, absolutely possible. That, that this can discover new science. I think we have a lot of work to do to formalize the science.[00:39:11] swyx: And then, I, I think the last part is you know, How much, how much do we cheat by gen, by generating data from Unreal Engine 5? Mm hmm. which is what a lot of people are speculating with very, very limited evidence that OpenAI did that. The strongest evidence that I saw was someone who works a lot with Unreal Engine 5 looking at the side characters in the videos and noticing that they all adopt Unreal Engine defaults.[00:39:37] swyx: of like, walking speed, and like, character choice, like, character creation choice. And I was like, okay, like, that's actually pretty convincing that they actually use Unreal Engine to bootstrap some synthetic data for this training set. Yeah,[00:39:52] Alessio: could very well be.[00:39:54] swyx: Because then you get the labels and the training side by side.[00:39:58] swyx: One thing that came up on the last day of February, which I should also mention, is EMO coming out of Alibaba, which is also a sort of like video generation and space time transformer that also involves probably a lot of synthetic data as well. And so like, this is of a kind in the sense of like, oh, like, you know, really good generative video is here and It is not just like the one, two second clips that we saw from like other, other people and like, you know, Pika and all the other Runway are, are, are, you know, run Cristobal Valenzuela from Runway was like game on which like, okay, but like, let's see your response because we've heard a lot about Gen 1 and 2, but like, it's nothing on this level of Sora So it remains to be seen how we can actually apply this, but I do think that the creative industry should start preparing.[00:40:50] swyx: I think the Sora technical blog post from OpenAI was really good.. It was like a request for startups. It was so good in like spelling out. Here are the individual industries that this can impact.[00:41:00] swyx: And anyone who, anyone who's like interested in generative video should look at that. But also be mindful that probably when OpenAI releases a Soa API, right? The you, the in these ways you can interact with it are very limited. Just like the ways you can interact with Dahlia very limited and someone is gonna have to make open SOA to[00:41:19] swyx: Mm-Hmm to, to, for you to create comfy UI pipelines.[00:41:24] Alessio: The stability folks said they wanna build an open. For a competitor, but yeah, stability. Their demo video, their demo video was like so underwhelming. It was just like two people sitting on the beach[00:41:34] swyx: standing. Well, they don't have it yet, right? Yeah, yeah.[00:41:36] swyx: I mean, they just wanna train it. Everybody wants to, right? Yeah. I, I think what is confusing a lot of people about stability is like they're, they're, they're pushing a lot of things in stable codes, stable l and stable video diffusion. But like, how much money do they have left? How many people do they have left?[00:41:51] swyx: Yeah. I have had like a really, Ima Imad spent two hours with me. Reassuring me things are great. And, and I'm like, I, I do, like, I do believe that they have really, really quality people. But it's just like, I, I also have a lot of very smart people on the other side telling me, like, Hey man, like, you know, don't don't put too much faith in this, in this thing.[00:42:11] swyx: So I don't know who to believe. Yeah.[00:42:14] Alessio: It's hard. Let's see. What else? We got a lot more stuff. I don't know if we can. Yeah, Groq.[00:42:19] Groq Math[00:42:19] Alessio: We can[00:42:19] swyx: do a bit of Groq prep. We're, we're about to go to talk to Dylan Patel. Maybe, maybe it's the audio in here. I don't know. It depends what, what we get up to later. What, how, what do you as an investor think about Groq? Yeah. Yeah, well, actually, can you recap, like, why is Groq interesting? So,[00:42:33] Alessio: Jonathan Ross, who's the founder of Groq, he's the person that created the TPU at Google. It's actually, it was one of his, like, 20 percent projects. It's like, he was just on the side, dooby doo, created the TPU.[00:42:46] Alessio: But yeah, basically, Groq, they had this demo that went viral, where they were running Mistral at, like, 500 tokens a second, which is like, Fastest at anything that you have out there. The question, you know, it's all like, The memes were like, is NVIDIA dead? Like, people don't need H100s anymore. I think there's a lot of money that goes into building what GRUK has built as far as the hardware goes.[00:43:11] Alessio: We're gonna, we're gonna put some of the notes from, from Dylan in here, but Basically the cost of the Groq system is like 30 times the cost of, of H100 equivalent. So, so[00:43:23] swyx: let me, I put some numbers because me and Dylan were like, I think the two people actually tried to do Groq math. Spreadsheet doors.[00:43:30] swyx: Spreadsheet doors. So, one that's, okay, oh boy so, so, equivalent H100 for Lama 2 is 300, 000. For a system of 8 cards. And for Groq it's 2. 3 million. Because you have to buy 576 Groq cards. So yeah, that, that just gives people an idea. So like if you deprecate both over a five year lifespan, per year you're deprecating 460K for Groq, and 60K a year for H100.[00:43:59] swyx: So like, Groqs are just way more expensive per model that you're, that you're hosting. But then, you make it up in terms of volume. So I don't know if you want to[00:44:08] Alessio: cover that. I think one of the promises of Groq is like super high parallel inference on the same thing. So you're basically saying, okay, I'm putting on this upfront investment on the hardware, but then I get much better scaling once I have it installed.[00:44:24] Alessio: I think the big question is how much can you sustain the parallelism? You know, like if you get, if you're going to get 100% Utilization rate at all times on Groq, like, it's just much better, you know, because like at the end of the day, the tokens per second costs that you're getting is better than with the H100s, but if you get to like 50 percent utilization rate, you will be much better off running on NVIDIA.[00:44:49] Alessio: And if you look at most companies out there, who really gets 100 percent utilization rate? Probably open AI at peak times, but that's probably it. But yeah, curious to see more. I saw Jonathan was just at the Web Summit in Dubai, in Qatar. He just gave a talk there yesterday. That I haven't listened to yet.[00:45:09] Alessio: I, I tweeted that he should come on the pod. He liked it. And then rock followed me on Twitter. I don't know if that means that they're interested, but[00:45:16] swyx: hopefully rock social media person is just very friendly. They, yeah. Hopefully[00:45:20] Alessio: we can get them. Yeah, we, we gonna get him. We[00:45:22] swyx: just call him out and, and so basically the, the key question is like, how sustainable is this and how much.[00:45:27] swyx: This is a loss leader the entire Groq management team has been on Twitter and Hacker News saying they are very, very comfortable with the pricing of 0. 27 per million tokens. This is the lowest that anyone has offered tokens as far as Mixtral or Lama2. This matches deep infra and, you know, I think, I think that's, that's, that's about it in terms of that, that, that low.[00:45:47] swyx: And we think the pro the break even for H100s is 50 cents. At a, at a normal utilization rate. To make this work, so in my spreadsheet I made this, made this work. You have to have like a parallelism of 500 requests all simultaneously. And you have, you have model bandwidth utilization of 80%.[00:46:06] swyx: Which is way high. I just gave them high marks for everything. Groq has two fundamental tech innovations that they hinge their hats on in terms of like, why we are better than everyone. You know, even though, like, it remains to be independently replicated. But one you know, they have this sort of the entire model on the chip idea, which is like, Okay, get rid of HBM.[00:46:30] swyx: And, like, put everything in SREM. Like, okay, fine, but then you need a lot of cards and whatever. And that's all okay. And so, like, because you don't have to transfer between memory, then you just save on that time and that's why they're faster. So, a lot of people buy that as, like, that's the reason that you're faster.[00:46:45] swyx: Then they have, like, some kind of crazy compiler, or, like, Speculative routing magic using compilers that they also attribute towards their higher utilization. So I give them 80 percent for that. And so that all that works out to like, okay, base costs, I think you can get down to like, maybe like 20 something cents per million tokens.[00:47:04] swyx: And therefore you actually are fine if you have that kind of utilization. But it's like, I have to make a lot of fearful assumptions for this to work.[00:47:12] Alessio: Yeah. Yeah, I'm curious to see what Dylan says later.[00:47:16] swyx: So he was like completely opposite of me. He's like, they're just burning money. Which is great.[00:47:22] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars[00:47:22] Alessio: Gemini, want to do a quick run through since this touches on all the four words.[00:47:28] swyx: Yeah, and I think this is the mark of a useful framework, that when a new thing comes along, you can break it down in terms of the four words and sort of slot it in or analyze it in those four frameworks, and have nothing left.[00:47:41] swyx: So it's a MECE categorization. MECE is Mutually Exclusive and Collectively Exhaustive. And that's a really, really nice way to think about taxonomies and to create mental frameworks. So, what is Gemini 1. 5 Pro? It is the newest model that came out one week after Gemini 1. 0. Which is very interesting.[00:48:01] swyx: They have not really commented on why. They released this the headline feature is that it has a 1 million token context window that is multi modal which means that you can put all sorts of video and audio And PDFs natively in there alongside of text and, you know, it's, it's at least 10 times longer than anything that OpenAI offers which is interesting.[00:48:20] swyx: So it's great for prototyping and it has interesting discussions on whether it kills RAG.[00:48:25] Alessio: Yeah, no, I mean, we always talk about, you know, Long context is good, but you're getting charged per token. So, yeah, people love for you to use more tokens in the context. And RAG is better economics. But I think it all comes down to like how the price curves change, right?[00:48:42] Alessio: I think if anything, RAG's complexity goes up and up the more you use it, you know, because you have more data sources, more things you want to put in there. The token costs should go down over time, you know, if the model stays fixed. If people are happy with the model today. In two years, three years, it's just gonna cost a lot less, you know?[00:49:02] Alessio: So now it's like, why would I use RAG and like go through all of that? It's interesting. I think RAG is better cutting edge economics for LLMs. I think large context will be better long tail economics when you factor in the build cost of like managing a RAG pipeline. But yeah, the recall was like the most interesting thing because we've seen the, you know, You know, in the haystack things in the past, but apparently they have 100 percent recall on anything across the context window.[00:49:28] Alessio: At least they say nobody has used it. No, people[00:49:30] swyx: have. Yeah so as far as, so, so what this needle in a haystack thing for people who aren't following as closely as us is that someone, I forget his name now someone created this needle in a haystack problem where you feed in a whole bunch of generated junk not junk, but just like, Generate a data and ask it to specifically retrieve something in that data, like one line in like a hundred thousand lines where it like has a specific fact and if it, if you get it, you're, you're good.[00:49:57] swyx: And then he moves the needle around, like, you know, does it, does, does your ability to retrieve that vary if I put it at the start versus put it in the middle, put it at the end? And then you generate this like really nice chart. That, that kind of shows like it's recallability of a model. And he did that for GPT and, and Anthropic and showed that Anthropic did really, really poorly.[00:50:15] swyx: And then Anthropic came back and said it was a skill issue, just add this like four, four magic words, and then, then it's magically all fixed. And obviously everybody laughed at that. But what Gemini came out with was, was that, yeah, we, we reproduced their, you know, haystack issue you know, test for Gemini, and it's good across all, all languages.[00:50:30] swyx: All the one million token window, which is very interesting because usually for typical context extension methods like rope or yarn or, you know, anything like that, or alibi, it's lossy like by design it's lossy, usually for conversations that's fine because we are lossy when we talk to people but for superhuman intelligence, perfect memory across Very, very long context.[00:50:51] swyx: It's very, very interesting for picking things up. And so the people who have been given the beta test for Gemini have been testing this. So what you do is you upload, let's say, all of Harry Potter and you change one fact in one sentence, somewhere in there, and you ask it to pick it up, and it does. So this is legit.[00:51:08] swyx: We don't super know how, because this is, like, because it doesn't, yes, it's slow to inference, but it's not slow enough that it's, like, running. Five different systems in the background without telling you. Right. So it's something, it's something interesting that they haven't fully disclosed yet. The open source community has centered on this ring attention paper, which is created by your friend Matei Zaharia, and a couple other people.[00:51:36] swyx: And it's a form of distributing the compute. I don't super understand, like, why, you know, doing, calculating, like, the fee for networking and attention. In block wise fashion and distributing it makes it so good at recall. I don't think they have any answer to that. The only thing that Ring of Tension is really focused on is basically infinite context.[00:51:59] swyx: They said it was good for like 10 to 100 million tokens. Which is, it's just great. So yeah, using the four wars framework, what is this framework for Gemini? One is the sort of RAG and Ops war. Here we care less about RAG now, yes. Or, we still care as much about RAG, but like, now it's it's not important in prototyping.[00:52:21] swyx: And then, for data war I guess this is just part of the overall training dataset, but Google made a 60 million deal with Reddit and presumably they have deals with other companies. For the multi modality war, we can talk about the image generation, Crisis, or the fact that Gemini also has image generation, which we'll talk about in the next section.[00:52:42] swyx: But it also has video understanding, which is, I think, the top Gemini post came from our friend Simon Willison, who basically did a short video of him scanning over his bookshelf. And it would be able to convert that video into a JSON output of what's on that bookshelf. And I think that is very useful.[00:53:04] swyx: Actually ties into the conversation that we had with David Luan from Adept. In a sense of like, okay what if video was the main modality instead of text as the input? What if, what if everything was video in, because that's how we work. We, our eyes don't actually read, don't actually like get input, our brains don't get inputs as characters.[00:53:25] swyx: Our brains get the pixels shooting into our eyes, and then our vision system takes over first, and then we sort of mentally translate that into text later. And so it's kind of like what Adept is kind of doing, which is driving by vision model, instead of driving by raw text understanding of the DOM. And, and I, I, in that, that episode, which we haven't released I made the analogy to like self-driving by lidar versus self-driving by camera.[00:53:52] swyx: Mm-Hmm. , right? Like, it's like, I think it, what Gemini and any other super long context that model that is multimodal unlocks is what if you just drive everything by video. Which is[00:54:03] Alessio: cool. Yeah, and that's Joseph from Roboflow. It's like anything that can be seen can be programmable with these models.[00:54:12] Alessio: You mean[00:54:12] swyx: the computer vision guy is bullish on computer vision?[00:54:18] Alessio: It's like the rag people. The rag people are bullish on rag and not a lot of context. I'm very surprised. The, the fine tuning people love fine tuning instead of few shot. Yeah. Yeah. The, yeah, the, that's that. Yeah, the, I, I think the ring attention thing, and it's how they did it, we don't know. And then they released the Gemma models, which are like a 2 billion and 7 billion open.[00:54:41] Alessio: Models, which people said are not, are not good based on my Twitter experience, which are the, the GPU poor crumbs. It's like, Hey, we did all this work for us because we're GPU rich and we're just going to run this whole thing. And

Garth Heckman  The David Alliance   TDAgiantslayer@Gmail.com           How to be a man March 26th GRUB@ 6:45, MTG @7:15 HillSpring Church. Looking for your thoughts ideas and topics. 200 E 280th New Prague MN HillSpringChurchNewPrague@Gmail.com    Gal. 6:6Nevertheless, the one who receives instruction in the word must share in all good things with his instructor. 7Do not be deceived: God is not to be mocked. Whatever a man sows, he will reap in return. 8The one who sows to please his flesh, from the flesh will reap destruction; but the one who sows to please the Spirit, from the Spirit will reap eternal life.…   What you want does not mean it is what you will get… But guaranteed, what you do get is what you attracted by choices you've made in your life.    What kind of body do you want? What are you sewing? What kind of wife? What are you sewing? What kind of relationships? What kind of finances?   I truly feel sorry for the girls who sleep around and are being the Bad girl, the hot chick the girl who is not afraid to “be herself”… but she will reap disaster in relationships and self esteem… the same is true for boys to men. Why is divorce so high… lots of reasons… but the major reason is in the late 50's men started sleeping around outside of marriage and sowing that into our nation.    When you sow the right things, when you make the right choices, the tough choices… you are investing into a beautiful future… if you sow by your feelings… you are creating bills for your emotions, body, mind and relationships that you will never be able to pay. They will bankrupt you.    I stood in front a group of hardened 12-17 year olds in a boys maximum jail/prison/boys home. I asked them one by one why they were in here… some with smirks, some with disdain gave me answers like “I raped a woman, I killed someone, I stole a car and hit someone…  The answers were graphic, they were hard to hear with the attitudes they shared them with… no remorse, almost proud of their actions…  When they were done sharing… I heard the Holy Spirit whisper something into my ear… I spoke these words. You are not here because you are a bad ass, and you did some crazy rebellious stunt, you are not here because you aint afraid of doing time.. you are not here because you got caught and the system is unfair… You are here for one reason. You all wussed out and took the easy way. You made the easy decision. You chickened out from doing what is really gutsy, really tough, really proves a man… When it came to read, study, go to School, when it came to treat people with respect you all chickened out… you did the easy thing. You chose to live by your feelings, by what was easiest.  Some of you are sowing the easy seeds…  Some are sowing the hard seeds… And some of us are now reaping the fruit of those in full.                      How to be a man March 26th GRUB@ 6:45, MTG @7:15 HillSpring Church. Looking for your thoughts ideas and topics. 200 E 280th New Prague MN HillSpringChurchNewPrague@Gmail.com   What is my life about How to get a job What does it mean to  be a man How to prove to my woman I am a man of God How to get a woman How to deal with a woman who won't give to the relationship How to deal with a woman who pouts and wants her way How to handle money How to heal from the past How to stay fresh spiritually, emotionally, mentally,  Whats more dangerous porn or alcohol Ego: My worst enemy  Mindsets are the Key Mentorship is the secret The invisible word Depression from lack of vision  Do you date your best friends X?          You need to be here! You need to invite your man friend! 

Lucas Collazo e Henrique Esteter recebem, Luiz Alves, gestor de portfólio da Versa Asset Management, ao vivo no episódio #231 do Stock Pickers.EQUIPE DE PRODUÇÃO Apresentação: Lucas Collazo e Henrique EsteterDireção de cortes, produção e edição: Leonardo Tanaka e Fabio TeixeiraProdução: Mariana Shimojo e Nando LimaRedes sociais: Olavo Granado________________Invista em uma cadeira de valor, escolha a Marelli Sophie Trader: Acesse https://www.loja.marelli.com.br/stockpickers e use o cupom DESCONTOSTOCKPICKERS para ganhar 5% de desconto + frete grátis para todo o Brasil________________Fundo Selection Stock Pickers:https://lp.infomoney.com.br/fundo-stock-pickers-presente-exclusivo-v1?utm_source=infomoney&utm_medium=stockpickers&utm_campaign=202208_infomoney_stockpickers-10082022_youtube

Dancers and UCLA Arts alumni Jackie Lopez ‘04, Harry Weston ‘12, and Alli Gray ‘10 join "Works In Progress" to discuss what initially drew them to Hip Hop; what they gained from their time at UCLA; the history and future of Versa-Style Dance Company, a non-profit organization and dance ensemble; and any advice they can provide to aspiring dancers.

Meet Felipe Freig, owner of Versa Homes a boutique custom home builder located in British Columbia, Canada.  With a captivating journey that began as a skilled tile setter and a detour into the realm of music, Felipe found his true calling when he established his renovation company in 2009. Set to do $35 million in revenue over the next three years, Felipe credits Versa Home's success to working on the business and not in it.     Here are a few key takeaways from the discussion with Felipe: Having a mentor to learn the business side of home building Transitioning from home renovations to custom home builds Realizing that quantity of jobs doesn't always equal more revenue Setting up business processes for eventual sale Focusing on video marketing   Visit the Versa Homes website here: https://versahomes.com/   Own a construction company and want to share your story? Apply to be on an upcoming episode of Builder Stories at https://www.builderstories.com

When you're the parent of a medically complex or disabled child, the experience touches every aspect of our life – including our faith, often in unique and surprising ways. Today, I'm sharing a vulnerable and tender episode, where I interviewed four different women, each coming to the table with their own different spiritual backgrounds, to share how their faith has changed since having a child with a medical complexity. Ali Miller, Rachel Alves, Melissa Kellylove, and Bethany Mikulis all share their unique experiences – from faith that strengthened, faith that changed, faith that diminished, and faith that never really existed at all. This episode has something for absolutely everyone, no matter your religious or spiritual background, and I'm so proud of how open and respectful each of these women were as they shared their stories. This is one episode you can't miss!   Links: Listen to Episode 83 Part 1 & Episode 83 Part 2 to hear An Evolution in Faith with Kimberly Arnold. Follow Ali Miller, Rachel Alves, Melissa Kellylove, and Bethany Mikulis on Instagram! Follow us on Instagram @the_rare_life! Donate to the podcast or Contact me about sponsoring an episode. Fill out our contact form to get a reminder about upcoming discussion meetings and the Skype link to join! Access the transcript on the website here. And if you love this podcast, please leave us a rating or review in your favorite podcast app!

“The really successful companies will be the ones that realise that they can't set a bunch of policies at a company level and think it's going to work for everybody; rather, they will have highly personalised employee experiences." This is a special episode only available to our podcast subscribers, which we call The Mini Chief. These are short, sharp highlights from our fabulous CEO guests, where you get a 5 to 10 minute snapshot from their full episode. Our latest guest is the CEO of The VERSA Group, Kath Blackham. Her full episode is titled Extracting huge value from diversity and inclusion, mental health in the workplace and using tech for good and you can find the full audio and show notes here:

“The really successful companies will be the ones that realise that they can't set a bunch of policies at a company level and think it's going to work for everybody; rather, they will have highly personalised employee experiences." In this episode of The Inner Chief podcast, I speak to Kath Blackham, CEO of The VERSA Group, on extracting huge value from diversity and inclusion, mental health in the workplace and using tech for good.

On today’s Network Break, Greg Ferro is joined by guest co-host Brad Casemore. You can follow Brad on his blog Crepuscular Circus. Greg and Brad discuss new capabilities in Juniper’s Apstra data center automation software, Versa partnering with Intel to put security software on a NIC, and Cisco buying Splunk for $28 billion. The Linux... Read more »

On today’s Network Break, Greg Ferro is joined by guest co-host Brad Casemore. You can follow Brad on his blog Crepuscular Circus. Greg and Brad discuss new capabilities in Juniper’s Apstra data center automation software, Versa partnering with Intel to put security software on a NIC, and Cisco buying Splunk for $28 billion. The Linux […] The post Network Break 448: Cisco Splashes Out $28 Billion For Splunk; OpenTofu Is Vegetarian Alternative To Terraform appeared first on Packet Pushers.

On today’s Network Break, Greg Ferro is joined by guest co-host Brad Casemore. You can follow Brad on his blog Crepuscular Circus. Greg and Brad discuss new capabilities in Juniper’s Apstra data center automation software, Versa partnering with Intel to put security software on a NIC, and Cisco buying Splunk for $28 billion. The Linux... Read more »

On today’s Network Break, Greg Ferro is joined by guest co-host Brad Casemore. You can follow Brad on his blog Crepuscular Circus. Greg and Brad discuss new capabilities in Juniper’s Apstra data center automation software, Versa partnering with Intel to put security software on a NIC, and Cisco buying Splunk for $28 billion. The Linux […] The post Network Break 448: Cisco Splashes Out $28 Billion For Splunk; OpenTofu Is Vegetarian Alternative To Terraform appeared first on Packet Pushers.

On today’s Network Break, Greg Ferro is joined by guest co-host Brad Casemore. You can follow Brad on his blog Crepuscular Circus. Greg and Brad discuss new capabilities in Juniper’s Apstra data center automation software, Versa partnering with Intel to put security software on a NIC, and Cisco buying Splunk for $28 billion. The Linux... Read more »

On today’s Network Break, Greg Ferro is joined by guest co-host Brad Casemore. You can follow Brad on his blog Crepuscular Circus. Greg and Brad discuss new capabilities in Juniper’s Apstra data center automation software, Versa partnering with Intel to put security software on a NIC, and Cisco buying Splunk for $28 billion. The Linux […] The post Network Break 448: Cisco Splashes Out $28 Billion For Splunk; OpenTofu Is Vegetarian Alternative To Terraform appeared first on Packet Pushers.