#codingassistant — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #codingassistant, aggregated by home.social.
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Reports of #code's death are greatly exaggerated
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In the world of AI and LLM every mistake, error or bug you run into is already a »classic«.
»You’re running into a classic ›columns depend on async data‹ vs ›columns must be stable for grid state‹ conflict.«
Okay, I feel less alone now, but this still doesn't help me solve the problem, haha.
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Claude Code rolls out a voice mode capability
Anthropic is bringing Voice Mode to Claude Code, the company’s AI coding assistant for developers. The launch of…
#NewsBeep #News #Artificialintelligence #AI #AIvoice #Anthropic #ArtificialIntelligence #CA #Canada #claudecode #codingassistant #Technology #voicemode
https://www.newsbeep.com/ca/512880/ -
https://www.europesays.com/ie/366474/ Claude Code rolls out a voice mode capability #AI #AIVoice #Anthropic #ArtificialIntelligence #ArtificialIntelligence #ClaudeCode #CodingAssistant #Éire #IE #Ireland #Technology #VoiceMode
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Question for #LLM haters:So I don’t really want to hate on LLMs or generative AI in this post, but I am curious about alternatives to LLMs for code generation. What I particularly have in mind is how LLMs are pretty good at calling up code examples and fitting them into your code base.
Has anyone tried just creating a very large database with millions of code examples or code snippets, perhaps tagged with keywords pertaining to what the code does, and letting people search code by tag and automatically paste it into your file? If StackOverflow has been mined for LLM training data, can’t we just take that StackOverflow training data and parse-out the code snippets and generate such a database?
If LLMs coding tools like #Cursor, #Claude, #Copilot, Cody, etc. are basically doing that using statistical algorithms, couldn’t we do the same thing with ordinary symbolic computation and a clever little editor plug-in with good UI/UX design for the copy-pasting?
Does anyone know if such tools/databases already exist?
I’ll tag @screwlisp and @kentpitman on this one because I am especially curious about what you think about it.
#tech #software #AskFedi #CodingAssistant #LLM #AI #GenerativeAI
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@regehr
🙏🏻 So true, so well said, so best practice: helpers should “never, ever touch the keyboard when they're helping a student with an assignment. not even once! because as soon as someone else is driving, it becomes real easy for the student to stop thinking and just let things happen.”AND, the corollary or by extension:
“kind of like what happens when we use a coding assistant.”
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People share the post by a senior #Google engineer that #GenAI produced a distributed system orchestrator as if that was surprising, or a sign of actual capabilities.
The #LLM training data includes many, many very well documented code bases for this type of project. Lots of user experiences. User and API documentation. Reviews. PRs. Patches. ADRs, KEPs, RFCs. Discussion forums, thoughts about alternatives, comparisons. The list goes on.
(1/3)
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#ZombieShooter #TaskManager #Kiro #KiroweenHackathon #Python #PyGame #psutil #DeveloperTools #VibeCoding #SteeringDocs #RAM #SystemAdmin #ProcessKiller #GameDev #AI #Programming #Coding #SoftwareDevelopment #Tech #Innovation #Frankenstein #Hackathon #Developer #Productivity #CodingTools #DevTools #ProgrammingTools #SoftwareEngineering #TechInnovation #AIAssistant #CodingAssistant #DeveloperExperience #CodingLife #TechLife #Software #AppDevelopment #ProgrammingLanguage #CodingSkills #TechSkills #
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#ZombieShooter #TaskManager #Kiro #KiroweenHackathon #Python #PyGame #psutil #DeveloperTools #VibeCoding #SteeringDocs #RAM #SystemAdmin #ProcessKiller #GameDev #AI #Programming #Coding #SoftwareDevelopment #Tech #Innovation #Frankenstein #Hackathon #Developer #Productivity #CodingTools #DevTools #ProgrammingTools #SoftwareEngineering #TechInnovation #AIAssistant #CodingAssistant #DeveloperExperience #CodingLife #TechLife #Software #AppDevelopment #ProgrammingLanguage #CodingSkills #TechSkills #
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#ZombieShooter #TaskManager #Kiro #KiroweenHackathon #Python #PyGame #psutil #DeveloperTools #VibeCoding #SteeringDocs #RAM #SystemAdmin #ProcessKiller #GameDev #AI #Programming #Coding #SoftwareDevelopment #Tech #Innovation #Frankenstein #Hackathon #Developer #Productivity #CodingTools #DevTools #ProgrammingTools #SoftwareEngineering #TechInnovation #AIAssistant #CodingAssistant #DeveloperExperience #CodingLife #TechLife #Software #AppDevelopment #ProgrammingLanguage #CodingSkills #TechSkills #
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#ZombieShooter #TaskManager #Kiro #KiroweenHackathon #Python #PyGame #psutil #DeveloperTools #VibeCoding #SteeringDocs #RAM #SystemAdmin #ProcessKiller #GameDev #AI #Programming #Coding #SoftwareDevelopment #Tech #Innovation #Frankenstein #Hackathon #Developer #Productivity #CodingTools #DevTools #ProgrammingTools #SoftwareEngineering #TechInnovation #AIAssistant #CodingAssistant #DeveloperExperience #CodingLife #TechLife #Software #AppDevelopment #ProgrammingLanguage #CodingSkills #TechSkills #
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#ZombieShooter #TaskManager #Kiro #KiroweenHackathon #Python #PyGame #psutil #DeveloperTools #VibeCoding #SteeringDocs #RAM #SystemAdmin #ProcessKiller #GameDev #AI #Programming #Coding #SoftwareDevelopment #Tech #Innovation #Frankenstein #Hackathon #Developer #Productivity #CodingTools #DevTools #ProgrammingTools #SoftwareEngineering #TechInnovation #AIAssistant #CodingAssistant #DeveloperExperience #CodingLife #TechLife #Software #AppDevelopment #ProgrammingLanguage #CodingSkills #TechSkills #
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Gemini code just removed its own AI disclosure from my README.md :)
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Một người dùng Mac Mini M4 (16GB) đang tìm kiếm các công cụ và mô hình AI để chạy cục bộ, hỗ trợ lập trình. Họ đã thử Continue.dev + Ollama + qwen2.5-coder:7B để tự động hóa code mẫu, script bash, Python cơ bản. Cần lời khuyên về cách hiệu quả hơn, mô hình tốt cho các tác vụ này (tốc độ >15 token/giây) và tích hợp tốt với VS Code.
#MacMini #M4 #AI #LocalLLaMA #CodingAssistant #Ollama #ContinueDev #TrợLýLậpTrình #MôHìnhAI #MáyChủCụcBộ #PhầnCứngApple
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Open Codex CLI: Local-First AI Coding CLI Emerges As Alternative to OpenAI Codex CLI
#OpenCodex #AICoding #LLM #LocalLLM #OpenSource #DeveloperTools #CLI #Python #Phi4Mini #Terminal #AI #CodingAssistant #CommandLine
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Beta 2 Release of Deepin Linux 23 includes two AI features https://www.linux-magazine.com/Online/News/Two-AI-features-Introduced-in-Deepin-Linux-23-Beta-2-Release #DeepinLinux #Artificialintelligence #OpenSource #AI #FOSS #desktop #CodingAssistant #image
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I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows: 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appears set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
Another downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by spending a few hundred bucks on a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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not gonna lie, I'm impressed. I just tried #CodingAssistant in #Xcode26 for the first time, asking it to generate #SwiftUI charts of sums by date and an average per day of week, all from an existing CoreData schema, and it pretty much just worked.
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I was very amused to learn that the "Devin" AI assistant for software engineering uses **print statements** to debug things. (At least in one example.)
Even LLM's won't use super high-powered debugging tools when a print statement is at hand!
That may be the most human thing I've ever seen in LLM do.
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I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appear set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
A downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by investing a few hundred bucks in a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appear set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
A downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by investing a few hundred bucks in a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows: 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appears set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
Another downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by spending a few hundred bucks on a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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I had an unsettling experience a few days back where I was booping along, writing some code, asking ChatGPT 4.0 some questions, when I got the follow message: “You’ve reached the current usage cap for GPT-4, please try again after 4:15 pm.” I clicked on the “Learn More” link and basically got a message saying “we actually can’t afford to give you unlimited access to ChatGPT 4.0 at the price you are paying for your membership ($20/mo), would you like to pay more???”
It dawned on me that OpenAI is trying to speedrun enshitification. The classic enshitification model is as follows 1) hook users on your product to the point that it is a utility they cannot live without, 2) slowly choke off features and raise prices because they are captured, 3) profit. I say it’s a speedrun because OpenAI hasn’t quite accomplished (1) and (2). I am not hooked on its product, and it is not slowly choking off features and raising prices– rather, it appear set to do that right away.
While I like having a coding assistant, I do not want to depend on an outside service charging a subscription to provide me with one, so I immediately cancelled my subscription. Bye, bitch.
But then I got to thinking: people are running LLMs locally now. Why not try that? So I procured an Nvidia RTX 3060 with 12gb of VRAM (from what I understand, the entry-level hardware you need to run AI-type stuff) and plopped it into my Ubuntu machine running on a Ryzen 5 5600 and 48gb of RAM. I figured from poking around on Reddit that running an LLM locally was doable but eccentric and would take some fiddling.
Reader, it did not.
I installed Ollama and had codellama running locally within minutes.
It was honestly a little shocking. It was very fast, and with Ollama, I was able to try out a number of different models. There are a few clear downsides. First, I don’t think these “quantized” (I think??) local models are as good as ChatGPT 3.5, which makes sense because they are quite a bit smaller and running on weaker hardware. There have been a couple of moments where the model just obviously misunderstands my query.
But codellama gave me a pretty useful critique of this section of code:
… which is really what I need from a coding assistant at this point. I later asked it to add some basic error handling for my “with” statement and it did a good job. I will also be doing more research on context managers to see how I can add one.
A downside is that the console is not a great UI, so I’m hoping I can find a solution for that. The open-source, locally-run LLM scene is heaving with activity right now, and I’ve seen a number of people indicate they are working on a GUI for Ollama, so I’m sure we’ll have one soon.
Anyway, this experience has taught me that an important thing to watch now is that anyone can run an LLM locally on a newer Mac or by investing a few hundred bucks in a GPU. While OpenAI and Google brawl over the future of AI, in the present, you can use Llama 2.0 or Mistral now, tuned in any number of ways, to do basically anything you want. Coding assistant? Short story generator? Fake therapist? AI girlfriend? Malware? Revenge porn??? The activity around open-source LLMs is chaotic and fascinating and I think it will be the main AI story of 2024. As more and more normies get access to this technology with guardrails removed, things are going to get spicy.
https://www.peterkrupa.lol/2024/01/28/moving-on-from-chatgpt/
#ChatGPT #CodeLlama #codingAssistant #Llama20 #LLMs #LocalLLMs #OpenAI #Python
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Open Codex CLI: Local-First AI Coding CLI Emerges As Alternative to OpenAI Codex CLI
#OpenCodex #AICoding #LLM #LocalLLM #OpenSource #DeveloperTools #CLI #Python #Phi4Mini #Terminal #AI #CodingAssistant #CommandLine
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Open Codex CLI: Local-First AI Coding CLI Emerges As Alternative to OpenAI Codex CLI
#OpenCodex #AICoding #LLM #LocalLLM #OpenSource #DeveloperTools #CLI #Python #Phi4Mini #Terminal #AI #CodingAssistant #CommandLine
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Open Codex CLI: Local-First AI Coding CLI Emerges As Alternative to OpenAI Codex CLI
#OpenCodex #AICoding #LLM #LocalLLM #OpenSource #DeveloperTools #CLI #Python #Phi4Mini #Terminal #AI #CodingAssistant #CommandLine
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Open Codex CLI: Local-First AI Coding CLI Emerges As Alternative to OpenAI Codex CLI
#OpenCodex #AICoding #LLM #LocalLLM #OpenSource #DeveloperTools #CLI #Python #Phi4Mini #Terminal #AI #CodingAssistant #CommandLine
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#GitHub #Copilot Enterprise and a new set of #StarCoder2 #LLMs released this week will add to an expanding array of #genAI #codingassistant tools but experts urge caution as enterprises look to adopt them. https://www.techtarget.com/searchsoftwarequality/news/366571641/New-Nvidia-GitHub-AI-coding-assistants-expand-devs-options
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Cần mô hình AI chuyên hỗ trợ lập trình như Python hoặc Flutter do khó khăn về trí nhớ (ADHD, đột quỵ). Người dùng tìm mô hình 30‑70B chuyên về coding. #AIforCoding #LậpTrình #TrợLýTríTuệ #LocalLLaMA #CodingAssistant
https://www.reddit.com/r/LocalLLaMA/comments/1o02ci4/llm_question/