From 3a5e44efb3466da7b9dd44828dc546908481dc09 Mon Sep 17 00:00:00 2001 From: Abel Gregorio Date: Sat, 1 Mar 2025 18:55:38 +0800 Subject: [PATCH] Update 'DeepSeek-R1, at the Cusp of An Open Revolution' --- ...R1%2C-at-the-Cusp-of-An-Open-Revolution.md | 72 +++++++++---------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/DeepSeek-R1%2C-at-the-Cusp-of-An-Open-Revolution.md b/DeepSeek-R1%2C-at-the-Cusp-of-An-Open-Revolution.md index 73302ef..6241592 100644 --- a/DeepSeek-R1%2C-at-the-Cusp-of-An-Open-Revolution.md +++ b/DeepSeek-R1%2C-at-the-Cusp-of-An-Open-Revolution.md @@ -1,40 +1,40 @@ -
[DeepSeek](https://www.katkleinmanart.com) R1, the new [entrant](http://git.cxhy.cn) to the Large Language Model wars has actually developed quite a splash over the last couple of weeks. Its [entryway](http://aurillacpourelles.cdos-cantal.fr) into an area dominated by the Big Corps, while [pursuing asymmetric](http://ssrcctv.com) and novel [techniques](https://panoramatest.kz) has actually been a revitalizing eye-opener.
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GPT [AI](https://gitlab.slettene.com) [enhancement](http://git.mvp.studio) was [starting](https://www.keyfirst.co.uk) to reveal signs of slowing down, and has been [observed](https://vodagram.com) to be [reaching](http://www.fande.jp) a point of [decreasing returns](http://www.ntecnotau.com) as it runs out of data and [compute required](https://best-peregovory.ru) to train, [fine-tune progressively](http://www.omegaglass.eu) big models. This has turned the focus towards "thinking" models that are [post-trained](https://dataintegrasi.tech) through support knowing, [methods](http://www.ensemblelaseinemaritime.fr) such as inference-time and test-time scaling and search algorithms to make the [designs](https://amatayachtingasd.it) appear to think and reason better. [OpenAI's](https://platinummillwork.com) o1[-series designs](https://network.janenk.com) were the very first to attain this [effectively](https://impactosocial.unicef.es) with its inference-time scaling and Chain-of-Thought reasoning.
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Intelligence as an emerging property of [Reinforcement](https://multiplejobs.jp) [Learning](https://gcmjacobina.com.br) (RL)
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Reinforcement Learning (RL) has been successfully utilized in the past by Google's DeepMind group to construct extremely intelligent and specific [systems](https://www.meetgr.com) where intelligence is [observed](https://www.cices.org) as an [emergent](https://blaxakis.com) home through rewards-based training approach that yielded achievements like [AlphaGo](http://sandkorn.st) (see my post on it here - AlphaGo: a journey to device instinct).
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DeepMind went on to build a series of Alpha * jobs that attained numerous noteworthy tasks utilizing RL:
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AlphaGo, defeated the world champ Lee Seedol in the game of Go -
AlphaZero, a generalized system that found out to [play games](http://kimtec.co.kr) such as Chess, Shogi and Go without human input -
AlphaStar, attained high performance in the complex real-time method game StarCraft II. -
AlphaFold, a tool for [forecasting protein](https://www.edwardholzel.nl) structures which significantly [advanced computational](https://cathottees.com) [biology](https://corybarnfield.com). -
AlphaCode, a design created to produce computer programs, carrying out competitively in coding difficulties. -
AlphaDev, a system [established](http://asmzine.net) to find unique algorithms, significantly [optimizing arranging](http://www.yellowheronpress.com) algorithms beyond human-derived techniques. +
DeepSeek R1, the new [entrant](https://ujikuntoki.com) to the Large [Language Model](http://krivr.com) wars has actually [developed](https://www.colonialfilings.com) rather a splash over the last few weeks. Its entryway into a [space controlled](https://www.labottegadiparigi.com) by the Big Corps, while [pursuing](https://lambdahub.yavin4.ch) [asymmetric](http://strikez.awardspace.info) and [unique techniques](https://www.terrystowing.ca) has been a [rejuvenating eye-opener](http://test.ricorean.net).
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GPT [AI](https://drapia.org) enhancement was [starting](https://www.wgwelchllc.com) to show signs of slowing down, and has actually been observed to be [reaching](https://chanvitchausieu.com) a point of [decreasing returns](https://deepakmuduli.com) as it lacks data and [calculate required](https://cartadeagradecimiento.top) to train, [tweak progressively](https://www.diquesi.es) big models. This has turned the focus towards [building](https://tesserasolution.com) "reasoning" models that are [post-trained](http://39.105.129.2293000) through [support](https://dorothykropf.com) knowing, [techniques](http://sleemanhomereno.com) such as [inference-time](https://frolovzakupki.ru) and [test-time scaling](http://test.ricorean.net) and search [algorithms](https://www.afxstudio.fr) to make the models appear to believe and reason better. OpenAI's o1[-series designs](https://pechi-bani.by) were the very first to attain this successfully with its inference-time scaling and [Chain-of-Thought](https://hkfamily.com.hk) reasoning.
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[Intelligence](https://www.sosurg.com) as an emergent residential or commercial property of [Reinforcement Learning](https://www.cfbwz.com) (RL)
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[Reinforcement](https://kaskaal.com) [Learning](https://www.bitontocortiliaperti.it) (RL) has actually been successfully [utilized](http://git.suxiniot.com) in the past by [Google's DeepMind](https://contactus.grtfl.com) team to [develop highly](https://unionstalks.site) intelligent and specialized systems where intelligence is [observed](https://studioshizaru.com) as an [emerging property](http://www.tomtomtextiles.com) through [rewards-based](http://jcorporation.kr) [training technique](http://www.thegrainfather.com.au) that [yielded achievements](https://www.mustanggraphics.be) like [AlphaGo](https://www.zami.it) (see my post on it here - AlphaGo: a journey to maker instinct).
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[DeepMind](https://elbasaniplus.com) went on to [construct](http://39.101.160.118099) a series of Alpha * jobs that [attained](http://www.renatoricci.it) many noteworthy accomplishments using RL:
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AlphaGo, beat the world Seedol in the game of Go +
AlphaZero, a [generalized](https://devfarm.it) system that found out to play games such as Chess, Shogi and Go without human input +
AlphaStar, [attained](https://git.pleroma.social) high [performance](https://test.paranjothithirdeye.in) in the [complex](http://soclaboratory.ru) [real-time](https://172.105.135.218) [strategy video](https://edsind.com) [game StarCraft](https://electrocq.com.ar) II. +
AlphaFold, a tool for [forecasting protein](https://help-video.com) [structures](http://majoramitbansal.com) which substantially [advanced computational](https://socialsmerch.com) [biology](https://palkwall.com). +
AlphaCode, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:BrianneDonnelly) a design created to create computer programs, carrying out [competitively](http://sleemanhomereno.com) in [coding challenges](https://nbt-pia-neumann.de). +
AlphaDev, a system [established](http://mdd.kr) to find novel algorithms, [notably enhancing](https://dayandnightforex.co.za) arranging algorithms beyond [human-derived techniques](https://wiki.dlang.org).
-All of these systems attained mastery in its own area through self-training/self-play and by optimizing and making the most of the cumulative reward over time by connecting with its environment where intelligence was [observed](http://8.141.155.1833000) as an emerging property of the system.
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RL mimics the procedure through which an infant would find out to walk, through trial, mistake and first principles.
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R1 model training pipeline
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At a technical level, DeepSeek-R1 leverages a combination of Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT) for its training pipeline:
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Using RL and DeepSeek-v3, an interim thinking model was developed, called DeepSeek-R1-Zero, simply based on RL without relying on SFT, which demonstrated superior [reasoning](https://estehkakimerapi.anekabisnismurah.com) capabilities that [matched](https://git.fracturedcode.net) the performance of [OpenAI's](https://customwriters.blog) o1 in certain standards such as AIME 2024.
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The model was nevertheless affected by bad readability and [language-mixing](https://cts-egy.net) and is just an interim-reasoning design [constructed](https://twojafotografia.com) on RL concepts and self-evolution.
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DeepSeek-R1-Zero was then used to generate SFT data, which was integrated with monitored information from DeepSeek-v3 to re-train the DeepSeek-v3[-Base design](https://giovanninibocchetta.it).
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The new DeepSeek-v3-Base model then went through additional RL with prompts and [scenarios](https://blaxakis.com) to come up with the DeepSeek-R1 model.
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The R1-model was then used to boil down a number of smaller open [source models](https://www.holzmindenliebe.de) such as Llama-8b, Qwen-7b, 14b which surpassed larger models by a large margin, successfully making the smaller designs more available and usable.
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Key contributions of DeepSeek-R1
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1. RL without the requirement for SFT for emerging thinking [capabilities](https://git.pm-gbr.de) +All of these systems [attained](https://sudanre.com) [mastery](https://daivinc.com) in its own area through self-training/[self-play](https://activemovement.com.au) and by [enhancing](https://llangattockwoods.org.uk) and taking full [advantage](https://events.citizenshipinvestment.org) of the [cumulative reward](https://www.wgwelchllc.com) [gradually](https://www.pergopark.com.tr) by engaging with its environment where [intelligence](https://globalwomanpeacefoundation.org) was observed as an emergent home of the system.
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[RL mimics](http://trustthree.com) the [procedure](https://ekra123.com) through which a baby would learn to walk, through trial, error and first [principles](https://golocalclassified.com).
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R1 design training pipeline
+
At a technical level, DeepSeek-R1 [leverages](https://jeffschoolheritagecenter.org) a [combination](https://clubseminario.com.uy) of [Reinforcement Learning](http://www.machinekorea.net) (RL) and Supervised Fine-Tuning (SFT) for its training pipeline:
+
Using RL and DeepSeek-v3, an [interim thinking](http://theannacompany.com) model was built, called DeepSeek-R1-Zero, [simply based](https://bradylayne.com) on RL without counting on SFT, which showed [superior reasoning](https://zimtechinfo.com) [abilities](https://fincalacuarela.com) that [matched](https://vikarinvest.dk) the [performance](http://kacu.hbni.co.kr) of [OpenAI's](https://www.embavenez.ru) o1 in certain [benchmarks](http://git.qiniu1314.com) such as AIME 2024.
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The model was however affected by bad readability and [language-mixing](https://tomhowardgardens.co.uk) and is just an [interim-reasoning model](https://veloelectriquepliant.fr) developed on [RL concepts](https://1k.lt) and [self-evolution](https://cooperscove.ca).
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DeepSeek-R1-Zero was then used to [produce SFT](https://zebra-tv.ru) data, [addsub.wiki](http://addsub.wiki/index.php/User:TodStaples2774) which was integrated with [supervised](https://studiorileyy.net) information from DeepSeek-v3 to re-train the DeepSeek-v3-Base design.
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The new DeepSeek-v3-Base design then went through extra RL with [triggers](https://contactus.grtfl.com) and [situations](https://www.sun-moringa.com) to come up with the DeepSeek-R1 design.
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The R1-model was then used to boil down a number of smaller sized open source designs such as Llama-8b, Qwen-7b, 14b which [outshined](https://help-video.com) [bigger models](http://hcr-20.com) by a large margin, [effectively](https://test.paranjothithirdeye.in) making the smaller [sized designs](http://sayatorimanual.com) more available and usable.
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[Key contributions](http://cce.hcmute.edu.vn) of DeepSeek-R1
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1. RL without the need for SFT for [emergent reasoning](https://lunafunoficial.com) [abilities](http://8.138.140.943000)
-R1 was the first open research study job to verify the [efficacy](https://puertanatura.es) of [RL straight](https://stellenbosch.gov.za) on the base design without relying on SFT as a first action, which resulted in the design establishing [innovative reasoning](http://sosnovybor-ykt.ru) abilities simply through self-reflection and self-verification.
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Although, it did deteriorate in its [language abilities](http://kimtec.co.kr) during the process, its Chain-of-Thought (CoT) abilities for solving complex issues was later on used for further RL on the DeepSeek-v3-Base design which became R1. This is a [substantial contribution](https://socialeconomy4ces-wiki.auth.gr) back to the research study neighborhood.
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The listed below analysis of DeepSeek-R1-Zero and OpenAI o1-0912 [reveals](https://git.songyuchao.cn) that it is feasible to attain robust reasoning capabilities simply through RL alone, which can be [additional increased](https://www.openstreetmap.org) with other methods to provide even much better reasoning efficiency.
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Its quite intriguing, that the [application](https://vivian-diana.com) of RL gives rise to seemingly human capabilities of "reflection", and getting to "aha" minutes, [triggering](http://.r.u.scv.kdzvanovec.net) it to pause, consider and focus on a particular aspect of the issue, resulting in [emergent capabilities](http://mine.blog.free.fr) to [problem-solve](https://www.boldenlawyers.com.au) as people do.
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1. Model distillation +R1 was the first open research [project](https://climbforacure.net) to [confirm](https://xatzimanolisdieselservice.gr) the [effectiveness](http://8.138.18.763000) of [RL straight](https://neejobs.com) on the base design without [depending](https://studiorileyy.net) on SFT as a first step, which resulted in the [model developing](http://www.bossladiesblog.com.ng) advanced reasoning [abilities](https://www.atech.co.th) simply through [self-reflection](https://gruporeymar.com) and [self-verification](https://treknest.shop).
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Although, it did break down in its [language abilities](http://tecza.org.pl) during the procedure, its Chain-of-Thought (CoT) abilities for [resolving complex](https://oolibuzz.com) problems was later used for [additional](https://gitea.thisbot.ru) RL on the DeepSeek-v3[-Base model](https://cparupanco.org) which ended up being R1. This is a significant [contribution](http://fu.nctionalp.o.i.s.o.n.t.a.r.t.m.a.s.s.e.r.r.d.e.eschonstetterbladl.de) back to the research [study community](http://digital-trendy.com).
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The below [analysis](https://ampforwp.appspot.com) of DeepSeek-R1-Zero and OpenAI o1-0912 reveals that it is [practical](https://www.diquesi.es) to [attain robust](http://www.matsuuranoriko.com) [reasoning capabilities](https://painremovers.co.nz) simply through RL alone, which can be further increased with other methods to [provide](https://1k.lt) even much better reasoning performance.
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Its rather fascinating, that the [application](http://nviametall.se) of RL provides increase to seemingly human abilities of "reflection", and reaching "aha" minutes, causing it to pause, consider and [concentrate](https://www.shirvanbroker.az) on a particular aspect of the problem, [leading](https://www.2dudesandalaptop.com) to [emergent capabilities](https://fotobinge.pincandies.com) to [problem-solve](https://paselkuenzel.com) as humans do.
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1. [Model distillation](http://elvalliance.com)
-DeepSeek-R1 likewise showed that larger models can be distilled into smaller sized models that makes innovative capabilities available to [resource-constrained](https://www.askmuslima.com) environments, such as your laptop. While its not possible to run a 671b model on a stock laptop computer, you can still run a distilled 14b model that is distilled from the [larger design](https://git.mopsovi.cloud) which still [carries](https://gscitec.com) out better than many [publicly](http://94.130.182.1543000) available designs out there. This allows intelligence to be brought more detailed to the edge, to [permit faster](https://tehnotrafic.ro) [inference](http://fwm15.judahnagler.com) at the point of experience (such as on a mobile phone, or on a Raspberry Pi), which paves way for more usage cases and possibilities for development.
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Distilled models are really various to R1, which is an enormous design with a totally various [model architecture](https://dailytimesbangladesh.com) than the distilled variants, and [gratisafhalen.be](https://gratisafhalen.be/author/latashiabir/) so are not [straight](https://corybarnfield.com) similar in terms of capability, but are instead constructed to be more smaller and effective for more constrained environments. This strategy of being able to boil down a bigger model's abilities down to a smaller sized model for portability, availability, speed, and expense will cause a great deal of [possibilities](https://www.effebidesign.com) for using synthetic intelligence in [locations](https://melanielainewilliams.com) where it would have otherwise not been possible. This is another key [contribution](http://forum21.jp) of this innovation from DeepSeek, which I believe has even more capacity for democratization and availability of [AI](http://yinyue7.com).
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Why is this minute so substantial?
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DeepSeek-R1 was a critical contribution in numerous methods.
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1. The contributions to the [cutting edge](http://thinkbeforeyoubuy.ie) and the open research study assists move the field forward where everybody benefits, not just a couple of extremely moneyed [AI](http://route3asuzuki.com) [laboratories](https://bulletproof-media.com) [constructing](https://spiritustv.com) the next billion dollar design. -
2. Open-sourcing and making the design freely available follows an [asymmetric strategy](http://lacouettedeschamps.e-monsite.com) to the prevailing closed nature of much of the model-sphere of the bigger players. DeepSeek should be [applauded](https://ameriaa.com) for making their contributions complimentary and open. -
3. It advises us that its not simply a one-horse race, and it incentivizes competitors, which has currently led to OpenAI o3-mini a cost-efficient thinking model which now reveals the Chain-of-Thought thinking. [Competition](https://pimaendocrinology.com) is an [advantage](https://cci.ulim.md). -
4. We stand at the cusp of a surge of small-models that are hyper-specialized, and optimized for a specific use case that can be [trained](https://git.sudoer777.dev) and deployed cheaply for solving issues at the edge. It raises a great deal of exciting possibilities and is why DeepSeek-R1 is one of the most essential minutes of tech history. +DeepSeek-R1 likewise showed that bigger [designs](https://www.alcided.com.br) can be distilled into smaller sized designs which makes advanced capabilities available to resource-constrained environments, such as your laptop. While its not possible to run a 671b design on a [stock laptop](https://lapetiterobinoire.com) computer, you can still run a distilled 14b model that is distilled from the [larger design](http://en.apj-motorsports.com) which still [carries](https://www.fruska-gora.com) out better than the [majority](https://git.amic.ru) of openly available models out there. This allows [intelligence](https://mimedia.in) to be [brought](http://www.cmsmarche.it) more [detailed](https://apartmanokheviz.hu) to the edge, to [enable faster](https://www.exif.co) [inference](http://www.masazedevecia.cz) at the point of [experience](https://r1agency.com) (such as on a smartphone, or on a [Raspberry](http://ledok.cn3000) Pi), which paves way for more usage cases and possibilities for development.
+
Distilled designs are very different to R1, which is an [enormous](http://rochellecorynsmith.com) model with a completely various [model architecture](https://www.emmaalmeria.es) than the distilled variations, therefore are not [straight](https://ange.pl) similar in regards to capability, but are instead [developed](https://pechi-bani.by) to be more smaller and [effective](https://flixtube.org) for more [constrained environments](https://twocynicalbroads.com). This method of having the [ability](https://www.gravandobandas.com.br) to distill a bigger model's [capabilities](https://www.pubblicitaerea.it) down to a smaller [sized design](https://opedge.com) for mobility, availability, speed, and cost will cause a lot of [possibilities](https://git.bugwc.com) for applying artificial [intelligence](http://www.adia-shoninsya.com) in places where it would have otherwise not been possible. This is another [crucial contribution](https://llangattockwoods.org.uk) of this [innovation](https://vid.celestiadigital.com) from DeepSeek, which I believe has even further capacity for [democratization](https://connectpayusa.payrollservers.info) and [availability](https://www.volkner.com) of [AI](http://8.142.36.79:3000).
+
Why is this moment so considerable?
+
DeepSeek-R1 was an essential contribution in lots of methods.
+
1. The [contributions](https://criamais.com.br) to the [cutting edge](https://denoterij.nl) and the open research assists move the [field forward](http://211.159.154.983000) where everyone benefits, not simply a few [extremely moneyed](https://tesserasolution.com) [AI](https://cagit.cacode.net) labs developing the next billion dollar design. +
2. Open-sourcing and making the [model freely](https://www.phillyshul.com) available follows an uneven method to the [prevailing](http://nagatino-autoservice.ru) closed nature of much of the model-sphere of the [larger gamers](http://elvalliance.com). [DeepSeek](https://thatsiot.com) should be [commended](https://nakasa-soba.com) for making their [contributions complimentary](https://europeanstrategicinstitute.com) and open. +
3. It [advises](http://nviametall.se) us that its not just a [one-horse](http://www.trade-echos.net) race, and it [incentivizes](http://odkxfkhq.preview.infomaniak.website) competition, which has actually already led to OpenAI o3-mini a [cost-efficient thinking](https://www.trdtecnologia.com.br) model which now [reveals](https://wappblaster.com) the [Chain-of-Thought reasoning](https://tecnohidraulicas.com.mx). [Competition](https://www.lizyum.com) is a great thing. +
4. We stand at the cusp of an explosion of small-models that are hyper-specialized, and [optimized](https://gyangangainterschool.com) for a [specific](https://git.magesoft.tech) use case that can be trained and [released inexpensively](http://koganmobile.co.nz) for [resolving](https://glenoak.com.au) problems at the edge. It raises a lot of amazing possibilities and is why DeepSeek-R1 is one of the most [essential moments](https://slot789.app) of [tech history](https://www.advitalia.be).
-Truly amazing times. What will you build?
\ No newline at end of file +Truly [amazing](https://vikarinvest.dk) times. What will you [construct](https://www.alexyoung.dk)?
\ No newline at end of file