3 money-saving free, open-source apps to try this weekend (May 15-17)


Every few months, it’s worth asking yourself how much of your software spending is actually necessary. Open-source apps have quietly closed the gap with many paid tools, and in some cases, they’ve gone even further than their commercial counterparts in specific, practical ways.

If you have some free time this weekend, here are three apps worth trying first. Each one helps cut a different kind of expense, and at least one of them is probably relevant to something you’re already paying for.

All of these apps work on Linux, Windows, and macOS.

Handbrake

Shrink your storage bill by re-encoding your videos

HandBrake is a free, open-source video transcoder. You can use it to convert video files between formats, rip DVDs and Blu-rays, or compress footage into smaller file sizes using a more efficient codec. The obvious cost savings come from skipping paid tools like Adobe Media Encoder or Wondershare UniConverter. But for some people, HandBrake can save hundreds of dollars a year.

For example, I travel frequently, and after every trip I come back with around 1TB of raw footage. Four trips a year means roughly 4TB of video annually. That kind of storage adds up quickly—whether you’re buying external hard drives or paying for cloud storage through services like Google One or iCloud.

What HandBrake does is convert that footage from H.264—the default codec used by most cameras and phones—to H.265 (HEVC), which is significantly more efficient. In my experience, that conversion reduces file sizes by around 75%. So instead of storing 4TB of footage every year, I only need about 1TB while maintaining nearly identical visual quality. That translates directly into lower storage costs over time.

Upscayl

Stop paying the 4K tax

Upscayl is a free, open-source AI image upscaler that runs entirely offline. It uses local AI models to increase an image’s resolution, making low-resolution images look sharper and more detailed. You can technically upscale images by up to 16x, though the app itself recommends staying at 4x or lower since artifacts become more noticeable beyond that point.

The obvious financial benefit to using Upscayl is avoiding paid tools like Topaz Gigapixel. But there’s a more practical use case here for people like me who download a lot of wallpapers.

You’ve probably noticed that many wallpaper websites offer 1080p downloads for free but lock the 4K versions behind a paywall. That becomes even more frustrating if you use an ultrawide monitor, where high-resolution wallpapers are already harder to find. With Upscayl, you can download the free 1080p version and upscale it yourself. The results are often good enough for desktop use, even on large ultrawide displays, without the obvious pixelation you’d get from standard image scaling.

There’s also a useful angle for AI image generation. On many platforms, generating a 4K image costs significantly more credits than generating one at 1080p. However, you’re most likely paying for the composition, lighting, and prompt result—not the raw resolution itself. As such, a smarter workflow is to generate the image at 1080p and upscale it afterward with Upscayl. That lets you stretch your subscription credits much further while still ending up with a high-resolution image.


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Ollama

Run basic LLM-powered automations for free

Ollama lets you run AI language models locally on your own hardware. That means no recurring subscriptions, no API fees, and no concerns about your data leaving your machine. However, most people who approach Ollama as a replacement for ChatGPT usually end up disappointed. Most local models that can realistically run on consumer hardware simply aren’t as capable as Claude or ChatGPT—even compared to the free tiers of those services.

But that’s also missing the point. Local LLMs don’t necessarily need to function as general-purpose chatbots. Where Ollama really shines is automation.

Ollama mimics OpenAI’s API structure, which makes it easy to plug local models into automation tools like n8n. Instead of paying per request to a cloud AI provider, you can run many of those workflows entirely on your own machine.

For example, I was previously spending around $5–$10 per month on OpenAI API credits for a handful of personal workflows:

  • Turning transcribed voice notes into structured notes in Obsidian
  • Creating Google Calendar events from voice commands
  • Analyzing and renaming screenshots for my articles
  • Reading photos of receipts and logging them into a budget spreadsheet
  • Sending natural-language commands to Home Assistant

Now, none of these tasks require frontier-level intelligence, and I’ve been able to offload all of them to a local Qwen3.5 9B model running through Ollama. Using 4-bit quantization, I get roughly 20–30 tokens per second on an NVIDIA GeForce RTX 3060 with 12GB of VRAM.


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These FOSS apps are even better if your PC has a GPU

While having a GPU isn’t technically necessary for running these FOSS apps, having one makes a lot of difference. You’ll see faster encodes, quicker upscales, and snappier inference. If you’ve got a discrete graphics card sitting in your machine, you’re already set up to get the most out of everything here.



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In short: Accel has raised $5 billion in new capital, comprising a $4 billion Leaders Fund V and a $650 million sidecar, targeting 20-25 late-stage AI investments at an average cheque size of $200 million. The raise follows standout returns from its Anthropic stake (invested at $183B, now valued near $800B) and Cursor (backed at $9.9B, now reportedly around $50B), and lands in a Q1 2026 venture market that deployed a record $297 billion.

Accel, the venture capital firm behind early bets on Facebook, Slack, and more recently Anthropic and Cursor, has raised $5 billion in new capital aimed squarely at AI. The raise, reported by Bloomberg, comprises $4 billion for its fifth Leaders Fund and a $650 million sidecar vehicle, positioning the firm to write average cheques of around $200 million into late-stage AI companies globally.

The fund lands in a venture capital market that has lost any pretence of restraint. Q1 2026 saw $297 billion flow into startups worldwide, 2.5 times the total from Q4 2025 and the most venture funding ever recorded in a three-month period. Andreessen Horowitz has raised $15 billion. Thrive Capital has closed more than $10 billion. Founders Fund is finishing a $6 billion raise. Accel’s $5 billion is substantial but not exceptional in a market where the biggest funds are measured in the tens of billions.

The portfolio that made the pitch

What distinguishes Accel’s fundraise is the portfolio it can point to. The firm invested in Anthropic during its Series G at a $183 billion valuation. Anthropic has since closed a round at $380 billion and is now attracting offers at roughly $800 billion, meaning Accel’s stake has more than quadrupled in value in a matter of months. Anthropic’s annualised revenue has hit $30 billion, a trajectory that no company in history has matched.

The firm’s bet on Cursor has been similarly well-timed. Accel backed the AI code editor in June 2025 at a $9.9 billion valuation. By November, Cursor had raised again at $29.3 billion. By March 2026, the company was reportedly in discussions at a valuation of around $50 billion. For a developer tool that barely existed two years ago, the appreciation is extraordinary.

Accel’s broader AI portfolio extends beyond these two headline positions. The firm has backed Vercel, the frontend deployment platform; n8n, an AI-powered automation tool; Recraft, a professional design platform; and Code Metal, which builds AI development tools for hardware and defence applications. In March 2026, Accel launched an Atoms AI programme in partnership with Google’s AI Futures Fund, selecting five early-stage companies from what it described as a global applicant pool focused on “white space” opportunities in enterprise AI.

The Leaders Fund model

Accel’s Leaders Fund series is designed for later-stage investments, the kind of large cheques that growth-stage AI companies now require. With an average investment size of $200 million and a target of 20 to 25 deals from the new $4 billion fund, the strategy is concentrated: a small number of high-conviction bets on companies that have already demonstrated product-market fit and are scaling revenue.

This is a different game from traditional venture capital. At $200 million per cheque, Accel is competing less with seed and Series A firms and more with the mega-funds, sovereign wealth funds, and corporate investors that have flooded into late-stage AI. The firm’s argument is that its early-stage relationships and technical evaluation capabilities give it an edge in identifying which companies deserve capital at scale, and in securing allocations in rounds that are massively oversubscribed.

Founded in 1983 by Arthur Patterson and Jim Swartz, Accel built its reputation on what the founders called the “prepared mind” approach, a philosophy of deep sector research before investments materialise. The firm’s most famous prepared-mind bet was its 2005 investment of $12.7 million for 10% of Facebook, a stake worth $6.6 billion at the company’s IPO seven years later. The question now is whether Accel’s AI bets will produce returns of comparable magnitude.

What the market is pricing

The sheer volume of capital flowing into AI venture funds reflects a market consensus that artificial intelligence will be the dominant technology platform of the next decade. The numbers are difficult to overstate. OpenAI raised $120 billion in 2026. Anthropic has raised more than $50 billion. xAI closed $20 billion. Waymo secured $16 billion. These are not venture-scale numbers; they are infrastructure-scale capital deployments that would have been unthinkable outside of telecommunications or energy a decade ago.

For limited partners, the investors who commit capital to venture funds, the logic is straightforward: the returns from AI’s winners will be so large that even paying premium valuations will generate exceptional multiples. Accel’s Anthropic position, where a single investment has appreciated several times over in months, is exactly the kind of outcome that makes LPs willing to commit $5 billion to a single firm’s next fund.

The risk is equally visible. Venture capital is a cyclical business, and the current fundraising boom has the characteristics of a cycle peak: record fund sizes, compressed deployment timelines, and a concentration of capital in a single sector. The last time venture capital raised this aggressively, during the 2021 ZIRP era, many of those investments were marked down significantly within two years. AI’s commercial traction is far stronger than the crypto and fintech bets that defined that earlier cycle, but the valuations being paid today leave little margin for error.

The concentration question

Accel’s fund also highlights a structural shift in venture capital. The industry is bifurcating into a small number of mega-firms that can write cheques of $100 million or more and a long tail of smaller funds that compete for earlier-stage deals. The middle ground, the traditional Series B and C investors, is being squeezed by mega-funds moving downstream and by AI companies that skip traditional funding stages entirely, going from seed round to billion-dollar valuations in 18 months.

For a firm like Accel, which operates across offices in Palo Alto, San Francisco, London, and India, the $5 billion raise is a bet that it can maintain its position in the top tier as fund sizes inflate and competition for the best deals intensifies. Its portfolio of 1,199 companies, 107 unicorns, and 46 IPOs provides a track record. But in a market where Anthropic alone could generate returns that justify an entire fund, the temptation to concentrate bets on a handful of AI winners is strong, and the consequences of getting those bets wrong are correspondingly severe.

The broader picture is that AI venture capital has entered a phase where the funds themselves are becoming as large as the companies they once backed. Accel’s $5 billion raise would have made it one of the most valuable startups in Europe just a few years ago. Now it is table stakes for a firm that wants to participate meaningfully in the rounds that matter. Whether this represents rational capital allocation or the peak of a cycle that will eventually correct is the question that every LP writing a cheque today is, implicitly or explicitly, answering in the affirmative.



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