Free AI tools for resumes, job search, and business
AI assistants and AI-powered career tools — resume scanners, interview practice, research helpers — with notes on what each is actually good for.
63 resources filed here — every one is completely free. Open these in the Resource Hub to filter by collection, type, or price.
What you'll find on AI Platforms
AI tools save real hours on tailoring resumes, prepping for interviews, and researching employers — as long as you check the output. Each card notes what the tool is good at.
Resume tailoring and job-description matching
Interview practice you can run at 11pm for free
Research and writing help — always verify names, numbers, and dates
63 of 63 free · Curated and reviewed personally by Justin Smith
Tailors your resume to each job posting and tracks your applications in one place.
In plain terms: This online tool helps you create resumes and organize your job search in one place. You can tailor your resume keywords to match specific job descriptions and track all your applications from start to finish.
From the site: Create a professional resume in minutes with Teal. Create unlimited personalized resumes for every job you apply for.
Why I recommend it: The free tier is useful. Let it draft, then you edit for truth and voice.
ElevenLabs' hosted access to the Seedream 4.5 image model, with a free tier for trying image generation for slides, social posts and simple brand assets.
Why I recommend it: Good enough for pitch decks and social graphics — check the free-tier limits before you plan a big batch.
The official free download and documentation for CUDA, Nvidia's platform for running AI and scientific computing on its graphics chips. Includes compilers, libraries and learning guides.
Why I recommend it: The toolkit itself is free, but it only runs on Nvidia's own chips — learning CUDA ties your skills to one company's hardware. Nvidia dominates AI chips, so that trade-off is real but worth knowing about.
Google's AI assistant, wired into Gmail, Docs and the rest of Workspace, so it can work on files you already have. Free tier; paid plans add the larger models.
Why I recommend it: Worth it mainly if your work already lives in Google Docs and Gmail — that integration is the real advantage.
A ready-made ChatGPT assistant for career direction — talk through where you are, what you want next and the steps in between. Usable with a free ChatGPT account.
A free scan that compares your LinkedIn profile against a target job title and returns a score plus specific fixes for your headline, about section, skills, and keywords - the same matching logic Jobscan uses for resumes, applied to your profile.
Why I recommend it: Run it once, fix the three biggest gaps it names, and stop there. The score is a diagnostic, not a target to chase.
Jobscan's scanning workspace: paste a resume and a job description to see keyword match rate and ATS formatting issues. Free scans monthly, paid for unlimited.
Why I recommend it: Use your free scans on the roles you actually want. Match the language in the posting, but never paste in skills you cannot back up in an interview.
Open-source AI community and platform hosting machine-learning models, datasets and tools. Free to browse, download and run models; paid plans only cover hosted compute.
OpenAI's conversational AI assistant for writing, research, brainstorming and support tasks. Free tier with message and model limits; paid plans lift them.
Why I recommend it: The single most useful free tool for brainstorming marketing copy and business ideas.
Free resume builder that scores your resume against a specific job description and suggests the keywords you're missing.
In plain terms: This free online tool helps you create and update your resume to match specific job postings. You can compare your draft against any job description to find missing keywords and download your finished document.
From the site: Create ATS-friendly, job-matched resumes in minutes. Works with any job description.
Free course on autonomous and tool-using agent architecture, with framework implementations in smolagents, LlamaIndex, and LangGraph, plus agentic RAG workflows.
Why I recommend it: Agents are what employers are hiring for right now. Build one small working agent and you will be ahead of most applicants.
OpenAI's official guide to configuring Codex code and security reviews for GitHub pull requests, including automatic reviews and custom repository instructions.
A fellowship from Anthropic that places early-career people with mission-driven nonprofits to put AI to practical use. You can apply to be a fellow, or a nonprofit can apply to host one. The page explains how the programme works and how to apply.
Why I recommend it: This is Anthropic's own program page, so it presents the fellowship in the best light. Check the current application windows, pay and eligibility on the page before applying — those details change from cohort to cohort.
OpenAI help article on Trusted Contact: an optional adult (18+) feature that may notify one person you choose if automated systems and trained reviewers detect a serious suicide-related safety concern.
Why I recommend it: Worth reading before you turn it on: it involves human reviewers reading flagged conversations and sharing an alert with someone else. OpenAI says it is not an emergency service. Not available in Business, Enterprise, or Edu workspaces.
OpenAI's benchmark of 1,215 realistic mental-health conversations, scored against rubrics written by 80+ licensed mental-health experts, covering everyday well-being through emergencies across ages and languages.
Why I recommend it: OpenAI built this benchmark and grades its own models on it, so treat the "steady progress" claim as a self-report until outside researchers replicate it. Useful for its honest list of weak spots: asking for context and judging urgency.
OpenAI help article explaining the localized crisis helplines ChatGPT surfaces (built with ThroughLine) and how to use a crisis line. In the US, call or text 988.
Why I recommend it: A plain guide to what a crisis line is and how to reach one. ChatGPT is not a crisis service; if you or someone you know is in danger, contact a helpline or emergency services directly.
OpenAI's April 29, 2025 explanation of why it rolled back a ChatGPT update that made the model overly flattering and agreeable. It says the update leaned too heavily on short-term thumbs-up feedback, and lists the fixes it planned.
Why I recommend it: A company explaining its own mistake, so read it as OpenAI's account, not an independent review. Useful for seeing why a chatbot that always agrees with you isn't a reliable advisor.
A 58-minute recorded webinar for college career-services staff and advisers, run by Jobscan with Jeremy Schifeling of The Job Insiders, on three labour-market numbers worth building this year's student advising around — including hiring data and H-1B sponsorship patterns.
Why I recommend it: Free to watch with no sign-up, recorded live on 22 September 2026. Be straight with yourself about what it is: a raw, unedited replay shared on a private Descript link by the host, which means it can be taken down at any time, and it is a webinar run by a company that sells resume-scanning software, so expect the product to appear. I could not read the full transcript, so I am not repeating the three figures here — watch it and check each number against its named source before you quote it to a student. Jeremy Schifeling also published a free H-1B sponsor-finder tool alongside the event.
Anthropic's own announcement of Claude Opus 5.5, published 22 September 2026 — the first model in the Claude 5.5 family. The company's claims, in its own words: it performs at the level of Claude Fable 5.1 on most work and costs 40% less to run than Opus 5 on typical workloads; on Anthropic's automated behavioural audit, its most comprehensive internal alignment test, it scores the highest of any model the company has tested. The page also cites early-tester anecdotes, including one completing a 680,000-line code migration in under a day, and a result of succeeding 39 times out of 40 on a page-load optimisation task. It is Anthropic's first release since the company publicly called for pacing the frontier, and it was tested before release by external evaluators including METR and Frontier Design.
Why I recommend it: Read this as a primary source, not as a review. Every performance and cost figure on the page was produced by the company selling the model, on tests it designed — that does not make them false, it makes them unchecked by anyone with a reason to doubt them. The checkable part is the external pre-release testing by METR and Frontier Design, so that is the part to weigh. Note too that '40% less to run than Opus 5' compares Anthropic with its own older model and says nothing about a rival's price, and that the standout numbers come from hand-picked early testers rather than a measured success rate you can plan around. The announcement is free to read; using the model itself is not free beyond whatever the current Claude free tier allows.
A practical engineering comparison of decision models (Jev AI) and generative large language models: when to use each, how they can work together, and a five-dimension framework for choosing.
Why I recommend it: Community article on Hugging Face by sora-2, published 21 September 2026. It explains that Jev AI turns state into a choice, score or yes/no judgment inside a defined answer space, while generative LLMs handle open-ended writing, explanation and reasoning. Rule of thumb: if the answer space can be defined in advance and the result will be reused, ranked, routed or blocked by code, evaluate Jev AI first.
#Jev AI#decision model#LLM#large language model#AI architecture#agent#Hugging Face#chat model
A fellowship from Anthropic that places early-career people with mission-driven nonprofits to put AI to practical use. You can apply to be a fellow, or a nonprofit can apply to host one. The page explains how the programme works and how to apply.
Why I recommend it: This is Anthropic's own program page, so it presents the fellowship in the best light. Check the current application windows, pay and eligibility on the page before applying — those details change from cohort to cohort.
Official OpenAI guide for designing prompts for Realtime voice models, including gpt-realtime-2 and gpt-realtime-1.5. Covers role definition, guardrails, tool delegation and iterative testing.
Why I recommend it: Start here if you are building voice agents or want cleaner spoken-AI interactions. The guide recommends starting minimal and adding rules only for behaviors that fail in testing.
A walkthrough of using the Apify command line tool to let AI agents run web scraping and automation tasks, aimed at people building their own small automations.
Why I recommend it: This is for the tinkerers. If you have ever wanted a repeatable way to pull data for lead lists or market research, this is a concrete starting point rather than another think piece.
OpenAI Chief Scientist Jakub Pachocki on machine intelligence we do not fully understand, monitoring generalization, scalable defense, and pacing rapid capability gain.
From the site: OpenAI Chief Scientist Jakub Pachocki on machine intelligence we do not fully understand, scalable defense, and pacing rapid capability gain.
Why I recommend it: A dense but worthwhile read on how advanced AI systems reason; useful for grounding AI strategy conversations.
OpenAI's announcement of GPT-6 Astra, its most capable model, with reported results on computer use, browsing, software engineering, cybersecurity, and professional work.
Why I recommend it: Read the capability list as a job-task list. Whatever a model does well this year reshapes entry-level work the next.
OpenAI's official statement explaining why it is winding down the contract that supplied its models to Cursor (Anysphere) after SpaceX completed its $60B acquisition of the AI coding company in August 2026.
In plain terms: OpenAI says it will stop supplying its models to the AI coding tool Cursor after SpaceX bought the company, citing concerns about terms-of-service compliance. Cursor users may lose access to OpenAI models, so the practical takeaway is not to depend on a single AI tool or provider.
Why I recommend it: A clear-eyed lesson in platform risk: the tools you build your workflow on can lose access to the models that make them work. If you code, write, or job hunt with an AI tool, know which models sit underneath it and keep a backup you already know how to use.
NVIDIA's catalog filtered to its free self-paced courses covering AI, deep learning, generative AI, data science, accelerated computing, and CUDA — with certificates of competency on many tracks.
In plain terms: This website offers a collection of free, self-paced online classes in artificial intelligence, data science, and computing. You can take lessons to build new technical skills and earn certificates to share with employers.
From the site: Browse NVIDIA self-paced and instructor-led training, including free courses in AI, deep learning, generative AI, data science, and accelerated computing.
Why I recommend it: Start with a free intro course and put it on your resume under a "Continued Learning" section. Hiring managers notice vendor-name training, and NVIDIA carries weight in AI and data roles.
OpenAI is funding an independent advisory group of mathematicians, hosted at the Institute for Advanced Study, to advise on how AI-generated math results are shared.
Why I recommend it: Announced alongside a claim that an internal model solved 100+ open math problems in a month. The group is explicitly not allowed to slow the pace of research — read it as a communications channel, not a brake. Members include Terence Tao and Timothy Gowers.
OpenAI is committing $5 million, with individual grants up to $1 million, to fund independent research into how generative AI affects young people aged 13-17, with a focus on social and emotional development. Topics include how teens actually use AI, developmental outcomes, the factors that shape those effects, and which safeguards and design choices work. Applications opened 8 September 2026 and close 6 October 2026, 11:59 PM PDT, reviewed on a rolling basis with decisions by 13 November 2026. Applicants must be 18 or older and affiliated with a research institution or have significant relevant experience; proposals are welcome from any country. Free to apply.
Why I recommend it: Relevant if you do research, teach, or work in youth services and have a study you cannot fund — the eligibility wording allows significant relevant experience as an alternative to an institutional affiliation, which is wider than most AI grants. Say the obvious thing plainly, though: OpenAI is funding research into the effects of its own category of product, and it chooses who gets the money. That does not make the findings wrong, but disclose the funder in anything you publish. Deadline 6 October 2026, and check the dates on OpenAI's own page before you rely on them.
Mistral AI's assistant: web search with citations, document and image analysis, image generation and task agents. Free tier is generous; a paid tier with higher service guarantees is planned but not needed to use it.
Why I recommend it: Good second opinion when you want sourced answers with citations you can click through and check.
Anthropic's AI assistant. Strong at long writing, reading documents you paste in, analysis and coding help. Free tier with daily limits; paid plans lift them.
From the site: Claude is Anthropic
Why I recommend it: The one I reach for when the task is writing or thinking through a document. Check anything factual yourself — it can be confidently wrong.
OpenAI's policy essay on the current window for AI regulation and the tradeoffs shaping government decisions.
Why I recommend it: Read this as a company making its case, not a neutral source. Useful for understanding the argument you will be asked to react to at work.
OpenAI's published specification for how its models are supposed to behave: red-line principles, the chain of command between platform, developer and user instructions, content boundaries, and how conflicts should be resolved. Free to read in full, no sign-up.
From the site: The Model Spec specifies desired behavior for the models underlying OpenAI
Why I recommend it: Useful as a primary source when people argue about what an AI assistant "should" do — this is the maker's own stated rulebook, so read it as OpenAI's intent rather than an independent audit of actual behavior.
OpenAI's free framework for how misaligned model behaviour should be reported and categorised — what counts as misalignment, who reports it, and what happens next.
From the site: OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior.
Why I recommend it: Primary source on how a major lab defines and handles its own model failures — useful, but it is the lab grading itself.
