Shadowing-Übung: 5 open source tools that replaced my $320/mo AI stack... - Englisch Sprechen Lernen mit Video
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Recently, I did some math, and it made me realize how broken the world is.
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A cursor, $20.
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Claude Max, $100.
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GPT Pro, another $100.
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Gemini Ultra, another $100.
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Not to mention 11 Labs, DeepSeq, and all the API keys I signed up for two years ago and forgot about.
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My AI addiction got so expensive that I had to quit alcohol and Zin.
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And I'm not the only one, because alcohol sales in the United States are currently sitting at historic lows.
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And Big AI is the one to blame for this.
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But that was my wake-up call, and since then, I've cancelled all these subscriptions, and now self-host the ultimate AI stack for developers that's not only cheaper, but far more productive.
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In today's video, we'll look at a bunch of free and open-source projects you can run on your own server.
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We'll make them work together, while still having the option to tap into the intelligence of big models like Claude and GPT Sol.
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It is September 7th, 2026, and you're watching The Code Report.
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The first tool we need to talk about is OLAMA, because every AI stack first needs a model.
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For most people, this means sending your proprietary code to a trillion dollar corporation and trusting them to keep it totally safe, or you can use OLAMA and do everything locally.
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I've talked about this tool many other times on the channel, and essentially it's like Docker for large language models.
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It gives you a simple command line interface and API for downloading and running models on your own machine.
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This is great if you want to try out the latest Chinese models or any other open weight models, and what's even better is that all your prompts stay private and the inference cost is zero,
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which means the model continues to work even after your credit card is declined.
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The big problem, though, is that you probably don't own the hardware to run anything close to state-of-the-art.
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The small models will run almost anywhere, but running a frontier-sized model would require a small data center, and that's a big problem if you're a Vibe engineer who needs to write top-tier slot.
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Luckily, there's a self-hostable solution to this called 9Router, which is an AI tool that sits between your AI tools and dozens of different model providers through one local endpoint.
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Instead of juggling nine different API keys, 9Router provides one door with an OpenAI compatible proxy that allows you to point everything through localhost.
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But the thing that's really cool about 9Router is fallback tiers.
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On tier one, you might have an existing subscription like Claude Max that you already pay for.
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Then on tier two, you might have a cheap pay-per-token OpenAI model as a backup.
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Then tier three has all of free providers like Chinese models, vertex trial credits, and so on.
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That means if you max out Claude Max, it'll automatically roll over to the next tier without you having to do anything.
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But in addition to that, 9Router also tracks usage and will compress your tool output to reduce token consumption, which will ultimately save you a bunch of money.
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But if you're still eating billions of tokens per day, then you need to know about Headroom, which is a context compression layer for AI agents.
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You ask AI to center a div, and to do that, it reads 50,000 lines of your package lock JSON file, then it evaporates an Olympic-sized swimming pool worth of water,
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just to realize it needs to install Tailwind CSS.
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Headroom sits between your application and the model provider,
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and compresses everything from tool outputs to log files and any other useless chunks before it actually goes to the model, which would be billable input tokens.
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And one clever design feature of this tool is that it's reversible.
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The compressed content that ships to the model is cached on your local machine, which means if the model ever needs it, it can always retrieve it.
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This sounds awesome, but at this point, you might be wondering, where do I deploy all this stuff?
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Well, the answer, of course, is Hostinger, the sponsor of today's video.
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Not only is it one of the most affordable places to host your own virtual private server, but they also have an entire Docker catalog built into the panel,
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which means every single open source project I talk about in this video can be hosted there with a single click.
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In fact, you could have all these tools running and working together on the same VPS.
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Head over to hostinger.com slash fireship and use my coupon code to save a bunch of money spinning up your own VPS.
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Once you have that done, the next tool you'll want to install there is Diffy.
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Everything we've talked about so far is infrastructure, but now it's time to build an actual app that'll make us rich.
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Instead of trying to engineer everything through prompts, Diffy is a visual builder that allows us to drag nodes on a canvas.
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This has been a game changer for Horse Tinder and its AI matchmaking feature.
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The app will send Diffy each horse's profile information, then a visual workflow retrieves compatible horses from the database, and uses a large language model to explain each match.
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Like Thunder and Butternuts are a 94% match because they both enjoy trail rides and biting children.
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Then the entire workflow is exposed as an API, so the front-end application can just call it whenever a horse swipes right.
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Amazing, but building apps as a human became outdated in Q2 2026 after the release of open hands
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which is an open source tool
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that allows you to fire yourself this tool is a top performing autonomous coding agent on swebench verified
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which is the benchmark where models fix real github issues instead of trying to build
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and fix things just open up github issues
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and let open hands do all the work essentially it gives
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you a command center where you can self-host an army of ai agents
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that are always on and working in the background and because Because it's running on your own virtual private server, you have the option to run it with OpenAI or Anthropic models,
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or use the local LLMs that you installed earlier with OLAMA.
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And now at this point, we have our own private AI stack that could build any type of software you can imagine.
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But this is just the tip of the iceberg, and there's tons of other open source applications in Hostinger's Docker catalog that you can install with zero hassle.
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Check out the link below
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and use coupon code Fireship to get your own virtual private server on Hostinger up and running right now.
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This has been the Code Report, thanks for watching, and I will see you in the next one.
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Wortschatz und Sprechhinweise zu dieser Lektion
Diese Sprechübung auf Niveau B2 basiert auf dem Video „5 open source tools that replaced my $320/mo AI stack..“. Diese Wörter kommen am häufigsten vor: model, tool, tier, code, private. Dieses Video enthält 63 Sätze und 1161 Wörter zum Nachsprechen. Der gesprochene Teil dauert 5:22. Der Sprecher spricht schnell, etwa 217 Wörter pro Minute – rechne mit verbundenen und verkürzten Lauten. Nur 80 % der Wörter gehören zu den 3.000 häufigsten im Englischen, der Wortschatz ist also anspruchsvoll.
Wichtiger Wortschatz in diesem Video
Die 15 anspruchsvollsten Wörter aus dem Video, mit Aussprache und Bedeutung:
| Wort | Aussprache | Bedeutung |
|---|---|---|
| tier Substantiv | /ˈtɪɚ/ | Schicht, Rang |
| token Substantiv | /ˈtoʊkən/ | Zeichen, Symbol |
| compress Verb | /ˈkɑmpɹɛs/ | komprimieren |
| stack Substantiv | /stæk/ | Stapel, Feime |
| provider Substantiv | /pɹəˈvaɪ.də(ɹ)/ | Versorger, Versorgerin |
| install Verb | /ɪnˈstɔl/ | installieren |
| hosting Substantiv | /ˈhoʊstɪŋ/ | Hosting |
| workflow Substantiv | /ˈwɝkfloʊ/ | Arbeitsablauf |
| retrieve Verb | /ɹɪˈtɹiːv/ | zurückholen, zurückerhalten |
| prompt Verb | /pɹɑmpt/ | veranlassen, dazu bringen |
| coupon Substantiv | /ˈkuːˌpɑn/ | Gutschein, Coupon |
| catalogue Substantiv | /ˈkæt.əˌlɔɡ/ | Katalog, Liste |
| compatible Adjektiv | /kəmˈpæt.ə.bəl/ | austauschbar, fähig |
| subscription Substantiv | /səbˈskɹɪpʃən/ | Abonnement, Bezug |
| cursor Substantiv | /ˈkɜːsə/ | Cursor, Läufer |
Phrasal Verbs, die du hören wirst
| Wort | Aussprache | Bedeutung |
|---|---|---|
| check out Verb | überprüfen | |
| open up Verb | öffnen | |
| try out Verb | ausprobieren, probieren | |
| wake up Verb | /ˌweɪk ˈʌp/ | aufwachen, erwachen |
Grammatik in diesem Video
Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:
| Struktur | Im Video |
|---|---|
| Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist | I've cancelled · I've talked · we've talked |
| Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut | is declined · is cached · can be hosted |
| Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache | consumption, which will · Headroom, which is · machine, which means |
Aussprache, auf die du achten solltest
Der Sprecher verwendet 13 Kurzformen und abgeschwächte Formen, zum Beispiel you're, I've, we'll. Sprich sie in der kurzen Form, so wie du sie hörst.
- Die Laute „sh“ und „zh“: subscription /səbˈskɹɪpʃən/, benchmark /ˈbɛn(t)ʃmɑːk/, cache /kæʃ/, compression /kəmˈpɹɛʃ.ən/, addiction /əˈdɪkˌʃən/
- Lange Wörter – auf die Betonung achten: compatible /kəmˈpæt.ə.bəl/, evaporate /ɪˈvæp.ə.ɹeɪt/, reversible /ɹəˈvɜː(ɹ)sɪbəl/, proprietary /pɹəˈpɹaɪ.ə.tə.ɹi/, autonomous /ɔˈtɑnəməs/
Laute, die Deutschsprachigen schwerfallen:
- /w/ — mit runden Lippen, nicht wie das deutsche „w“ (/v/): workflow /ˈwɝkfloʊ/, swipe /swaɪp/
- Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: retrieve /ɹɪˈtɹiːv/, catalogue /ˈkæt.əˌlɔɡ/, iceberg /ˈaɪsbɝɡ/, outdated /aʊtˈdeɪtɪd/, node /noʊd/
- /æ/ — offener als „ä“: stack /stæk/, catalogue /ˈkæt.əˌlɔɡ/, compatible /kəmˈpæt.ə.bəl/, evaporate /ɪˈvæp.ə.ɹeɪt/, fallback /ˈfɔːl.bæk/
So übst du mit diesem Video
- Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
- Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
- Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie tier, token, compress.
Was ist die Shadowing-Technik?
Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.