ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: 5 open source tools that replaced my $320/mo AI stack...

กำลังสร้างบทเรียน...
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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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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้

บทเรียนฝึกพูดระดับ B2 นี้ใช้วิดีโอ “5 open source tools that replaced my $320/mo AI stack..” เป็นสื่อ คำที่ถูกพูดซ้ำบ่อยที่สุด: model, tool, tier, code, private วิดีโอนี้มี 63 ประโยค และ 1161 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 5:22 ผู้พูดพูดเร็ว ประมาณ 217 คำต่อนาที จึงมีการเชื่อมเสียงและลดเสียงค่อนข้างมาก มีเพียง 80% ของคำที่อยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ คำศัพท์จึงค่อนข้างยาก

คำศัพท์สำคัญในวิดีโอนี้

คำที่ยากที่สุด 10 คำในวิดีโอ พร้อมคำอ่านและความหมาย:

คำศัพท์คำอ่านความหมาย
coupon คำนาม/ˈkuːˌpɑn/คูปอง
catalogue คำนาม/ˈkæt.əˌlɔɡ/แคตตาล็อก, รายการ
evaporate คำกริยา/ɪˈvæp.ə.ɹeɪt/ระเหย
iceberg คำนาม/ˈaɪsbɝɡ/ภูเขาน้ำแข็ง
proxy คำนาม/ˈpɹɑk.si/ตัวแทน
builder คำนาม/ˈbɪl.dəɹ/ผู้สร้าง, ผู้ก่อสร้าง
canvas คำนาม/ˈkæn.vəs/ผ้าใบ
expose คำกริยา/ɪkˈspoʊz/เผย, เปิดเผย
mathematics คำนาม/mæθ(.ə)ˈmæt.ɪks/คณิตศาสตร์
cancel คำกริยา/ˈkæn.sl̩/ขีดฆ่า

กริยาวลีที่คุณจะได้ยิน

คำศัพท์คำอ่านความหมาย
try out คำกริยาลอง
wake up คำกริยา/ˌweɪk ˈʌp/ตื่น, ตื่นนอน

ไวยากรณ์ในวิดีโอนี้

โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:

โครงสร้างในวิดีโอ
Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบันI've cancelled · I've talked · we've talked
Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำis declined · is cached · can be hosted
Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของconsumption, which will · Headroom, which is · machine, which means

การออกเสียงที่ควรระวัง

ผู้พูดใช้รูปย่อและรูปลดเสียง 13 ครั้ง เช่น you're, I've, we'll ให้พูดแบบสั้นตามที่ได้ยิน

  • เสียง “sh” และ “zh”: subscription /səbˈskɹɪpʃən/, benchmark /ˈbɛn(t)ʃmɑːk/, cache /kæʃ/, compression /kəmˈpɹɛʃ.ən/, addiction /əˈdɪkˌʃən/
  • คำยาว — ลงเสียงหนักให้ถูกพยางค์: compatible /kəmˈpæt.ə.bəl/, evaporate /ɪˈvæp.ə.ɹeɪt/, reversible /ɹəˈvɜː(ɹ)sɪbəl/, proprietary /pɹəˈpɹaɪ.ə.tə.ɹi/, autonomous /ɔˈtɑnəməs/

เสียงที่คนไทยมักออกเสียงยาก:

  • เสียงท้ายคำ — ออกเสียงให้ครบ ไม่เปลี่ยนเป็นตัวสะกดแบบไทย: compress /ˈkɑmpɹɛs/, install /ɪnˈstɔl/, retrieve /ɹɪˈtɹiːv/, compatible /kəmˈpæt.ə.bəl/, billable /ˈbɪləbəl/
  • /v/ — ไม่ใช่ /w/ ให้ฟันบนแตะริมฝีปากล่าง: provider /pɹəˈvaɪ.də(ɹ)/, retrieve /ɹɪˈtɹiːv/, evaporate /ɪˈvæp.ə.ɹeɪt/, reversible /ɹəˈvɜː(ɹ)sɪbəl/, vertex /ˈvɝˌtɛks/
  • /r/ กับ /l/ — แยกให้ชัด: reversible /ɹəˈvɜː(ɹ)sɪbəl/, builder /ˈbɪl.dəɹ/

วิธีฝึกกับวิดีโอนี้

  1. ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
  2. เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
  3. อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง coupon, catalogue, evaporate เป็นพิเศษ

เทคนิค Shadowing คืออะไร?

Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ

เทคนิค shadowing: อ่านคู่มือฉบับเต็มทีละขั้นตอน →