TL;DR
Welcome back to Dev Opportunity Radar.
This is a weekly series where I share opportunities, resources, communities, and interesting finds that I come across, with the goal of helping people discover things they might otherwise miss.
This week's edition features Pear Prime 2027, Hacktoberfest 2026, and Graph Hacks: Context for AI Agents, along with How to Scale Your Model as a resource worth checking out.
If you're new to the series, you can also browse previous editions, search past opportunities, and explore Community Finds, Reader Updates, and Resources Worth Checking Out on the Dev Opportunity Radar website. I've also written a short post about why I built it. You'll find links to both at the end of this article.
If you've discovered something through the Radar, I'd love to hear about it. Whether you applied to an opportunity, joined a community, completed a program, or found a resource you hadn't seen before, I'd be happy to feature your experience in a future 💙 Reader Updates section (with your permission).
And if you've come across an opportunity, resource, community, program, or event that deserves more attention, feel free to share it in the comments.
If I feature one of your Community Finds in a future edition, I'll always make sure to credit you. If you discovered it, that recognition belongs to you.
Table of Contents
⚡ Quick Scan
Opportunities
| Opportunity | Organization | Type | Location / Format | Deadline |
|---|---|---|---|---|
| Pear Prime 2027 | Pear | Early-Career Program | Hybrid / Community + Career | January 4, 2027 |
| Hacktoberfest 2026 | MLH × DEV | Open Source Event | Online + In-person | October 2026 |
| Graph Hacks: Context for AI Agents | WeMakeDevs × FalkorDB | AI Hackathon | Online (Worldwide) | October 18, 2026 |
Resource Highlight
| Resource | Why Check It Out |
|---|---|
| How to Scale Your Model | A free online book covering the systems side of scaling language models, including TPUs, GPUs, sharding, parallelism, training, inference, and performance. |
📝 A quick note: I spend a lot of time researching and verifying every opportunity before featuring it in Dev Opportunity Radar. However, deadlines, eligibility, program details, and application requirements can change after publication. Before applying, please take a few minutes to visit the official program page, review the latest information, and confirm that you're eligible.
🔄 Still Open From Previous Editions
Before we get into this week's opportunities, here are a few from the two most recent editions that are still accepting applications.
As the Radar has grown, there are now quite a few opportunities spread across the different editions. I used to keep all the still-open opportunities here, but I don't think repeating a long list every week makes the editions easier to read.
So I keep this section to the two most recent editions as a quick reminder of opportunities you may have missed. Older opportunities are still available on the Dev Opportunity Radar website, where you can browse the full collection and use the filters to find something specific.
I've already covered the opportunities below in detail, so I won't repeat everything here. If any of them catch your attention, you can go back to the original edition for the full overview, eligibility details, and application links.
| Opportunity | Organization | Type | Format | Deadline | Featured In |
|---|---|---|---|---|---|
| Nebius x NVIDIA Global AI Hackathon | Nebius × NVIDIA | Hackathon | Online | October 30 | Edition #18 |
| Build, Ship, Shape: Amazon Developer Hackathon | Amazon | Hackathon | Online | October 24 | Edition #17 |
| Dev3Pack Hackathon | Dev3Pack | Hackathon | Hybrid | October 30 | Edition #17 |
📍 This Week's Opportunities
Here are a few opportunities I came across this week that I thought were worth sharing.
📌 Pear Prime 2027
Who it's for: CS students, new graduates, and early-career engineers.
What it's about: Pear Prime is a program from Pear for a small cohort of early-career engineers. The program connects participants with founders and technical leaders from startups and technology companies, along with mentorship and career support from the Pear team.
For the 2027 cohort, Pear lists companies including Cerebras, Etched, Perplexity, HeyGen, Baseten, Forus, Vals, and Andera as teams that Prime Engineers can get exposure to.
The program also includes a community of other Prime Engineers, mentorship and career coaching, access to Pear investors for guidance and feedback on ideas, and priority consideration for PearX if members later decide to start a company.
The application is fairly focused on what you've built and where you want to go next. You'll be asked about your most significant project, internship, or research experience, technologies you're strongest in, notable achievements, the type of engineering work you enjoy, and what you're hoping to build or become over the next few years.
There is also a question about something you built or shipped because you wanted to, rather than because it was required by a class, job, or manager. So having personal projects, open-source work, research, or other examples of self-directed work could be useful to have ready when applying.
Format: Hybrid / Community + career opportunities
Program: Pear Prime 2027
Who can apply: CS students, new graduates, and early-career engineers
Application Deadline: January 4, 2027
Focus: Engineering careers, startups, mentorship, community, and building
Application: Resume + project/research experience + technical interests + career goals + other background information
🔗 Learn More | 🔗 Apply
📌 Hacktoberfest 2026
Who it's for: Anyone interested in open source, open-source AI, or building with open-weight models.
What it's about: Hacktoberfest 2026 is a month-long celebration of open source happening throughout October, with both online and in-person activities.
This year's Hacktoberfest is focused on open-source AI and open-weight models, with Fests happening in cities around the world as well as online activities that anyone can join.
If you're participating online, there are livestreams, Global Hack Week, and DEV Challenges, with a new mini-hackathon launching on DEV each week during October.
You can participate online from anywhere, attend an in-person Fest, or do both. And you don't need to be an experienced open-source contributor to take part.
One thing that's different this year is that pull requests and merge requests no longer count toward Hacktoberfest rewards. The event is instead focusing more on learning, building, and participating in the different activities.
Since I'm here on DEV and Hacktoberfest is powered by MLH and DEV, I wanted to give it a small mention here too. If you're already part of the DEV community, you might want to take a look at the DEV Challenges happening throughout October.
Of course, as always, it's completely up to you whether any of the activities are something you'd like to take part in. I just wanted to make sure people here knew it was happening.
Format: Online + In-person | When: October 2026
Cost: Free
Focus: Open source, open-source AI, and open-weight models
Online Activities: DEV Challenges, livestreams, and Global Hack Week
In-Person: 300+ Fests worldwide
🔗 Explore Hacktoberfest 2026
🔗 See DEV Challenges
📌 Graph Hacks: Context for AI Agents
Who it's for: Developers, AI builders, and anyone interested in building AI agents.
What it's about: Graph Hacks: Context for AI Agents is an online hackathon by WeMakeDevs and FalkorDB running from October 15–18, 2026.
The challenge is to build an AI agent or group of agents that uses FalkorDB as the primary graph database. The project should use the graph as part of what the agent does, such as working with connected data, remembering information between sessions, sharing state between agents, or connecting company knowledge.
There are three tracks:
- Agents That Act on Connected Data
- Agent Memory and Coordination
- Company Brain
You can participate in one, two, or all three tracks. You can join solo or with a team of up to four, and you don't need previous graph database experience.
One thing to keep in mind is that FalkorDB must be a central part of the project, and the main FalkorDB implementation needs to be new work completed during the hackathon. Coding assistants are allowed.
There is a $20,000 prize pool, with prizes for each track, along with prizes for the best project blog posts and the top 50 submissions.
Format: Online | Worldwide | Dates: October 15–18, 2026
Team Size: Solo or up to 4
Cost: Free
Tracks: 3
Graph Experience: Not required
AI Coding Assistants: Allowed
Deadline: October 18, 2026
📚 Resources Worth Checking Out
Not every useful find comes with an application deadline.
Here's one resource worth checking out this week.
How to Scale Your Model
If you're interested in understanding what happens when language models have to run across large amounts of hardware, How to Scale Your Model is a free online book from researchers at Google DeepMind and other institutions that takes a systems view of scaling LLMs on TPUs and GPUs.
The book looks at how hardware and models interact, including compute, memory, communication, model parallelism, training, inference, and scaling across multiple devices.
It starts with topics like roofline analysis, how TPUs work, and sharded matrix multiplication, then moves into Transformer math, training and inference parallelism, practical LLaMA 3 tutorials, profiling TPU code, JAX, and a bonus section on GPUs.
Some of the topics covered include data, tensor, pipeline, and expert parallelism, along with techniques such as model sharding, rematerialization, host offload, and gradient accumulation.
The resource is fairly technical. It assumes that you already understand the basics of LLMs and the Transformer architecture, and ideally have some familiarity with JAX. So this is probably more useful if you're already comfortable with the fundamentals and want to understand what happens underneath the model when it needs to scale.
The book is organized into 12 chapters, and the authors mention that you don't have to read everything in order. The first few chapters introduce the underlying concepts, while later sections apply them to real models and practical systems.
Format: Free online book
Focus: LLM systems, TPUs, GPUs, Transformers, training, inference, parallelism, and performance
Level: Advanced / technical
Prerequisites: Basic LLM and Transformer knowledge; JAX familiarity is helpful
Topics: Roofline analysis, TPU architecture, sharding, Transformer math, training, inference, profiling, JAX, GPUs
Authors: Researchers and engineers from Google DeepMind, Stanford, and other organizations
🔗 Read How to Scale Your Model
🌟 Community Finds
One of my favorite things about this series has been seeing people share opportunities, communities, and resources that others might not have discovered otherwise.
We've had some really wonderful community finds over the last few weeks, and I honestly can't thank everyone enough for taking the time to find these opportunities, look through them, and send them my way. I know that takes time, and I appreciate every person who thinks of the Radar when they come across something they feel others might find useful 💙
This week, we weren't able to include a community find, but that's completely okay. Hopefully we'll have more wonderful finds from the community as we keep going.
So if you come across an opportunity, fellowship, grant, hackathon, conference, community, resource, or anything else you think more people should know about, please do send it my way. I love seeing what you find, and it helps make this series better for everyone.
If I feature something you shared in a future edition, I'll always make sure to credit you. If you discovered it, that recognition belongs to you.
One small request: If you're sharing an opportunity, please avoid posting raw URLs directly in the comments. DEV sometimes filters them before I get a chance to see them.
A short description alongside the link makes it much easier for me to review and potentially feature it in a future edition.
💙 Reader Updates
I'm looking forward to this section gradually growing over time, and I'd still love to hear from you.
One of my favorite parts of writing Dev Opportunity Radar has been hearing from people who discovered something they otherwise might have missed.
If you discovered an opportunity through the radar, applied to something, joined a community, attended an event, or simply found a resource you hadn't seen before, I'd genuinely love to hear about it.
You don't need to have been accepted or have a big success story to share. Sometimes simply discovering the right opportunity at the right time is already a win.
If you'd like to share an update, feel free to leave a comment on this edition. With your permission, I may feature it in a future 💙 Reader Updates section and tag you so other readers can celebrate your journey too.
I hope this section gradually becomes a place where we can celebrate those stories together, one update at a time.
👋 Until Next Friday
Before I go, I just want to say thank you.
Every week, I spend time searching for opportunities, resources, and communities that I think deserve a little more attention. But one of my favorite parts of this series isn't the research. It's seeing what happens after an edition is published.
Seeing someone discover an opportunity, apply to a program, share a resource, suggest a Community Find, or come back to tell us what they learned reminds me why I started Dev Opportunity Radar in the first place.
The goal has always been simple:
Help people discover opportunities they otherwise might have missed.
Thanks to all of you, it feels like we're doing exactly that.
Whether you've been reading since the very first edition or this is your first time here, thank you for being part of the journey. Every comment, suggestion, and shared opportunity helps make this series better than I could build on my own.
If you ever come across an opportunity, resource, event, community, or anything else you think deserves more attention, I'd love for you to share it in the comments. And if Dev Opportunity Radar helped you discover something exciting, I'd love to hear that too.
Thank you for reading, for sharing, and for helping make this our radar, not just mine.
I'll be back next Friday with more opportunities, resources, and Community Finds.
Until then, take care, and I hope you discover something amazing this week 💙
🌐 Dev Opportunity Radar Website
If this is your first time discovering the series, you can explore every edition, browse opportunities by category, discover Community Finds, and catch up on Reader Updates on the Dev Opportunity Radar website.
👉 Website
I also wrote a short post about why I built the website and the journey behind it.
Top comments (8)
Thank you so much for doing this! I'm sure it takes a ton of work. Your efforts are greatly appreciated. Congrats to 19 editions!
Thank you so much 🧡 It definitely takes some time, but seeing readers find something useful through the Radar makes it all worth it 😃
Hema, 19 editions in, that is real consistency. Graph Hacks is a direct fit for me this time. The Agent Memory and Coordination track matches my MCP and retrieval work closely. To answer your question, I would love to see more hackathons built around agent infrastructure and memory systems, not just agent apps on top of someone else's stack. That is the layer I work in most. Thank you for still doing this every Friday.
Thank you so much, Daniel 😀 I’ll keep this in mind for sure while searching, and if I come across anything like this, I’ll include it!
Wow 19 editions already!! Congrats!
Thanks for sharing, I will try to read How to Scale Your Model.
Thank you for being here and supporting the Radar from the very beginning, Julien 💙
Hope you enjoy the "How to Scale Your Model" book 😀
What opportunities, communities, grants, fellowships, hackathons, conferences, or resources have you come across recently that deserve more attention?
I'm always looking for things to include in future editions, so feel free to share anything interesting you've found. If I feature one of your finds in a future edition, I'll always make sure to credit you.
If you discovered an opportunity through the radar, applied to something, joined a community, attended an event, or simply found something you hadn't seen before, I'd genuinely love to hear about that too. With your permission, I'd be happy to feature your update in a future 💙 Reader Updates section.
Small request: If you are sharing a link, please avoid posting raw URLs directly. DEV sometimes filters them before I get a chance to see the comment.
Also, We’re already 19 editions in!
I wanted to ask you all something. What kind of opportunities would you love to see more of in the Radar?
More Fellowships? Hackathons? AI opportunities? Learning programs? Communities? Or something completely different?
I can’t promise I’ll be able to find more of a specific type, since it really depends on what’s available and whether I think it’s actually useful enough to include. But if you tell me what you’d like to see more of, I’ll definitely keep it in the back of my mind while I’m searching each week.
Even if just a few of you share what you’re looking for, it would mean a lot to me.