Luyện nói tiếng Anh bằng Shadowing qua video: 5 open source tools that replaced my $320/mo AI stack...
Đang tạo bài học...
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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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Từ vựng và ghi chú luyện nói cho bài học này
Bài luyện nói trình độ B2 này dựa trên video “5 open source tools that replaced my $320/mo AI stack..”. Những từ được nhắc lại nhiều nhất trong bài: model, tool, tier, code, private. Video này có 63 câu và 1161 từ để luyện shadowing. Phần lời nói dài 5:22. Người nói nói nhanh, khoảng 217 từ mỗi phút, nên sẽ có nhiều chỗ nối âm và nuốt âm. Chỉ 80% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất, nên từ vựng khá khó.
Từ vựng quan trọng trong video
15 từ khó nhất trong video, kèm phiên âm và nghĩa:
| Từ | Phiên âm | Nghĩa |
|---|---|---|
| tier danh từ | /ˈtɪɚ/ | tầng, bậc |
| stack danh từ | /stæk/ | chồng |
| install động từ | /ɪnˈstɔl/ | cài đặt |
| compatible tính từ | /kəmˈpæt.ə.bəl/ | tương thích, tương hợp |
| cursor danh từ | /ˈkɜːsə/ | con trỏ |
| endpoint danh từ | đầu mút, mút | |
| evaporate động từ | /ɪˈvæp.ə.ɹeɪt/ | bốc hơi, bay hơi |
| iceberg danh từ | /ˈaɪsbɝɡ/ | núi băng trôi, băng sơn |
| juggling danh từ | /ˈd͡ʒʌɡəlɪŋ/ | tung hứng |
| matchmaking danh từ | /ˈmæt͡ʃˌmeɪkɪŋ/ | làm mối |
| cache danh từ | /kæʃ/ | bộ nhớ đệm, vùng nhớ đệm |
| chunk danh từ | /t͡ʃʌŋk/ | mảnh, mẩu |
| builder danh từ | /ˈbɪl.dəɹ/ | chủ thầu |
| autonomous tính từ | /ɔˈtɑnəməs/ | tự trị |
| verify động từ | /ˈvɛɹɪfaɪ/ | xác minh, xác thực |
Cụm động từ bạn sẽ nghe
| Từ | Phiên âm | Nghĩa |
|---|---|---|
| check out động từ | tính tiền, trả phòng | |
| open up động từ | mở | |
| wake up động từ | /ˌweɪk ˈʌp/ | thức dậy, dậy lên |
Ngữ pháp trong video
Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:
| Cấu trúc | Trong video |
|---|---|
| Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại | I've cancelled · I've talked · we've talked |
| Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm | is declined · is cached · can be hosted |
| Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật | consumption, which will · Headroom, which is · machine, which means |
Phát âm cần chú ý
Người nói dùng 13 dạng rút gọn, ví dụ you're, I've, we'll. Hãy nói theo dạng ngắn đúng như bạn nghe.
- Âm “sh” và “zh”: subscription /səbˈskɹɪpʃən/, benchmark /ˈbɛn(t)ʃmɑːk/, cache /kæʃ/, compression /kəmˈpɹɛʃ.ən/, addiction /əˈdɪkˌʃən/
- Từ dài — đặt trọng âm cho đúng: compatible /kəmˈpæt.ə.bəl/, evaporate /ɪˈvæp.ə.ɹeɪt/, reversible /ɹəˈvɜː(ɹ)sɪbəl/, proprietary /pɹəˈpɹaɪ.ə.tə.ɹi/, autonomous /ɔˈtɑnəməs/
Những âm người Việt thường gặp khó:
- Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: prompt /pɹɑmpt/, inference /ˈɪn.fə.ɹəns/, vertex /ˈvɝˌtɛks/, chunk /t͡ʃʌŋk/, mathematics /mæθ(.ə)ˈmæt.ɪks/
- Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: changer /ˈt͡ʃeɪnd͡ʒɚ/, juggling /ˈd͡ʒʌɡəlɪŋ/, matchmaking /ˈmæt͡ʃˌmeɪkɪŋ/, chunk /t͡ʃʌŋk/
- Âm /æ/ — mở miệng rộng hơn âm “e”: stack /stæk/, catalogue /ˈkæt.əˌlɔɡ/, compatible /kəmˈpæt.ə.bəl/, evaporate /ɪˈvæp.ə.ɹeɪt/, fallback /ˈfɔːl.bæk/
Cách luyện với video này
- Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
- Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
- Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như tier, stack, install.
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.